[{"run_id":"20260906T231458-5fdd2fff","semester":"1272","observed_at":"2026-09-06 23:14:58.172943+00:00","record_version_id":"4edf8117f1cfef1b8b8730da8c5810c2b342d7fe3ca26f9dc36834dcc20643c2","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"20260907T155543-ce3781c4","semester":"1272","observed_at":"2026-09-07 15:55:43.033547+00:00","record_version_id":"4edf8117f1cfef1b8b8730da8c5810c2b342d7fe3ca26f9dc36834dcc20643c2","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-03b0bbb9723907cf3d1b41cf0bb34837dc398e42","semester":"1262","observed_at":"2025-04-16 05:20:33.104844+00:00","record_version_id":"63f2b2b8259abeaeb4f7aeabdb0b159a6d555f44b205d19cd544b30bced18214","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-0837245627eb714ae6bb0375f7abe8f41042011f","semester":"1262","observed_at":"2025-04-16 05:20:33.104844+00:00","record_version_id":"63f2b2b8259abeaeb4f7aeabdb0b159a6d555f44b205d19cd544b30bced18214","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-08c92e359bbbc110b0778b7e0b15d6f40000e6b1","semester":"1262","observed_at":"2025-08-21 07:05:14.730696+00:00","record_version_id":"d9bc66922aa44c41c5e31d637c8f41131badca887d039fecd89c48882b96f108","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-0944d76440dc778fbb89626058c10451009ce9c6","semester":"1262","observed_at":"2025-05-21 03:36:18.194118+00:00","record_version_id":"16ab3ecb7de92244dd7434f7eac4c5a1e8a036b29b6ff7f45185af45804e570b","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-0a7f4bce29c816ef4d3d71b4eb8a2a3e15e33cc1","semester":"1262","observed_at":"2025-06-18 20:04:02.695444+00:00","record_version_id":"f586db80ce183dca04fc730635cfc7d0a00b39d90402ae63c20db8317b497ddf","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-0ab917f827a7de22ebdc1087114d377a31c4687b","semester":"1262","observed_at":"2025-05-22 02:11:02.531191+00:00","record_version_id":"d8f742c1945247d2c2d58b657c11550abb7814b0471284dbedeaa2c7704dda44","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-0c40ecab4b6db9333d1f18aef816e3ee659e300c","semester":"1262","observed_at":"2025-05-25 18:02:34.797617+00:00","record_version_id":"731945add160714c7115322fcae496907aae99e85757a83126fbd61751e090ca","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-0cb4d29a588ec7f11c7e00b7cf5dc3a0d23d0349","semester":"1266","observed_at":"2026-01-22 01:00:26.555427+00:00","record_version_id":"b335e0442c65d0fbc399f9860ec3d4736c03679df97d45ca04cd6499ab49a2d5","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-11c30ec39e91498cbfcf3a923c53a4b03e387280","semester":"1262","observed_at":"2025-07-20 04:58:26.847117+00:00","record_version_id":"3194eb7c9251a6687e02b338299af10dc6e6b81beab2ae854b8ee57e653b39b5","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-13cd500edf2587e9a3428f326253738fbea965ea","semester":"1264","observed_at":"2025-12-14 04:50:04.653658+00:00","record_version_id":"620ad3166395a7c2ef9ebd50808f229fea4a9eeb1d39a2af0c82c001ee2f793d","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-1da08b2de39f05e6bc877c189fc2fd6eeda563f5","semester":"1262","observed_at":"2025-07-01 00:03:56.707251+00:00","record_version_id":"dbfbd1f9b111033cee4b1fdf12dc8b38085643ed7b7725611da31abe8ab9c695","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-2031af47efe133b0e7877f433286ff07385afc41","semester":"1262","observed_at":"2025-07-02 20:16:45.749584+00:00","record_version_id":"93c95e5b696fcc94b99e785b15c8f87891fe131cc1b8dd69463ad427eb408b39","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-238eba6f32fbc30407f616dd0628ff02a2a824dd","semester":"1262","observed_at":"2025-08-17 05:15:07.788352+00:00","record_version_id":"834cbd5189d62349dee1a864924169f79085a503f03e6159c2a4e3c51e337a25","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-25f0ff56bc4d80f8a8afa1a0bcce8dae6fddb5e5","semester":"1262","observed_at":"2025-05-25 17:20:25.942090+00:00","record_version_id":"d56e0c721916bad7fcabc9757a976d17855063bbb6223509a2f1a90f3bb235b9","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-2923f91826ce253b7aeefa9f75b4a39fe4c80ff0","semester":"1262","observed_at":"2025-08-10 05:10:25.938416+00:00","record_version_id":"f0a90053644a8c9aab3f4322a569b82e01165887f319fb0a8da75fd787e83f3c","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-2a05214a4023e0157fa6f7d188c85308f714176c","semester":"1262","observed_at":"2025-09-07 04:56:38.255315+00:00","record_version_id":"13e4f27db6a2ed47c336f2be452fffeff3f9e9aff4aefb413200e22fe76e0909","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-2b1b8e39131de92b94620b25c5bbb25690efed4c","semester":"1262","observed_at":"2025-06-01 07:54:53.913454+00:00","record_version_id":"f26999925c2546bb0f0afce90a4fe23aa489bf371d57ac34b88b8f87fc292c6f","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-33a1225644e5a5855d60f66e65413946e72fedf4","semester":"1262","observed_at":"2025-06-22 01:52:05.152650+00:00","record_version_id":"a6e796f69943ae4ad56bd443c9c431e1162400fcb8d0bc9c442dbf7625f1bc2b","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-3b89c83dbf5208e13756184de1f2b2d0c09af35f","semester":"1262","observed_at":"2025-06-30 06:40:10.235522+00:00","record_version_id":"d1dff37483446b51cb44461e79bcc837ebaced7622f88750538f9548f6187dd8","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-3c3eebf7f35c39364b445a2fa5a112ce28dbb4a1","semester":"1262","observed_at":"2025-06-01 17:03:59.062985+00:00","record_version_id":"98e56b9f71fd32263a908a48c6ea55d7d70182f298a7dddfcef43104e2cbb95d","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-3fa4dd71391f1c9cf4ab2216680bbc428689f92d","semester":"1262","observed_at":"2025-05-25 01:15:58.180530+00:00","record_version_id":"6c8a44aa2d3faca9351e19fc4672cfaf20ec5a2f62d0abfa9afec0d703e6f16f","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-4040aa216da722a34483f68fc1e129ea571ae08d","semester":"1262","observed_at":"2025-06-01 07:27:39.695506+00:00","record_version_id":"d9a87a583a881c80b98b0d54f11a2086d0870925422ad4ef873d3cd7370f3579","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-454d53cd690294459f8ad0793cc3ed562060e56c","semester":"1262","observed_at":"2025-05-25 01:34:00.536004+00:00","record_version_id":"a868238081224da896251cdfba64a340900f54a0123947dfced9be01830c3d2f","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-486f091e051fdb4ccdb80ee7b4fb0a5814510324","semester":"1264","observed_at":"2025-11-16 05:32:31.273726+00:00","record_version_id":"784423612217047df4f59f46793125bcc17692e82112a3a44571ca390313002a","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-50393cfc5fd5598d4f4731ef1919f2ff53af07d9","semester":"1262","observed_at":"2025-06-30 06:05:57.438846+00:00","record_version_id":"8c67af2423b67701b8c2368c377e7967ab8678fa96b23a82c7399059de79d7f9","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-50f6947786b208c800db8bbd0cec84a8f0f187d2","semester":"1262","observed_at":"2025-06-02 03:49:42.915428+00:00","record_version_id":"196b2b91598fec5c016b19c037aab89282ecfc2caae9f1d55bc3e544d1c92689","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-51d86ed4082dced3803c295419b0ec8440942016","semester":"1264","observed_at":"2025-11-12 04:50:41.141307+00:00","record_version_id":"aa3fc385138d50e1d1074a350c06ecff8f2108f980ca5bd7339251374a65920f","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-53101bd1c10db0d00d094e4c9154d4c244ff23d9","semester":"1262","observed_at":"2025-10-05 04:58:05.366968+00:00","record_version_id":"198826d8efd4c69e6f651cb96857e7168fb7c96703c4bd9e8d90907dc8c0b1b1","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-58751151c564e11c5cb8aedd3a2d3527c22f73eb","semester":"1262","observed_at":"2025-06-01 06:59:01.536576+00:00","record_version_id":"aee8f6f5318b684bf67a70a8743502cd893557dc198c7c10b26496002181c2cc","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-5c4c8d3701a333ba4ddadef7d0b5dde8440fcdf0","semester":"1262","observed_at":"2025-06-01 01:57:51.256704+00:00","record_version_id":"128f978c64436f9a5816a3d07a0e3dc8700186b471c656ff74c45c67b4766fdd","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-5d20d9f81651d3c83fcaa2fd10870ce6e5d5a685","semester":"1262","observed_at":"2025-05-21 16:48:48.484741+00:00","record_version_id":"a7d2f140ece77b0176f059bcc385b877d39ca62c5d69dd3095dbd62d391223e9","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-5feaebf2ab37051c86ad360cc0fa0d7e0e2ea295","semester":"1262","observed_at":"2025-05-24 15:58:23.378859+00:00","record_version_id":"cd546bd8bbea69a61573564abed61e684c055709e2f0607bc5caed0a46217908","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-60949965544fce098cc006354e201149005fb77b","semester":"1264","observed_at":"2025-10-26 05:07:14.077864+00:00","record_version_id":"ecc7bf5e61f068b6ca9ac9cdaf53367d8d43fbe7a181721ba8a23422ed95b7e1","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-639225ac3addf8522f1d30cb4fe26054ea7f8c49","semester":"1262","observed_at":"2025-06-01 08:14:42.497608+00:00","record_version_id":"cb3bf38b4377fdd4641084a5a4198b8f4afefbd796995f36a1541a1839b7603c","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-64093b17b3a9c67a41aa33eb37adb53ec38df714","semester":"1262","observed_at":"2025-05-22 02:11:02.531191+00:00","record_version_id":"f167d7dd8c07c3840ac7b2a0c51f80f22ee507aaa091dfcb45e9d91a35e1d07d","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-6885b56099f55e08aa0205546369fb0381d50e13","semester":"1262","observed_at":"2025-07-13 04:59:22.367394+00:00","record_version_id":"cec1ce75cdc51b1c6ae36b271d401b1818903170aef0cb96e6c6fcf37f45a7f3","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-69d4b2b16e8268f75b6390b3a0e2ce8bf73999da","semester":"1262","observed_at":"2025-06-30 03:21:29.925498+00:00","record_version_id":"9fdf72cc7de4628b9f3bb7bec7f765b811b2dba19168f2d2782da05326d5d20a","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-6b29c3e0673b2f9a736d2363db5409e03d5b3ed4","semester":"1264","observed_at":"2025-12-03 12:33:41.418072+00:00","record_version_id":"a23ea7ac735009f49f470f59409b30fe70a3c7b763e67b8905e013152f49a797","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-73259984748de484976ef2ad56ad7ab3d4cb4518","semester":"1262","observed_at":"2025-06-26 08:27:56.461216+00:00","record_version_id":"c11e9c2632dd71e534090700966542fd86c00f1585829799f44a41583ac52b0a","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-741602fbc6252c232a0b9e6bc1a9ae3101625f5a","semester":"1262","observed_at":"2025-06-30 22:49:32.522034+00:00","record_version_id":"8d459884889547d4d465c0e2fb3f0e723a570fc16911f589396c255da2287097","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-7774f45558276ec29bf626e58fffb4a27bdbb2a9","semester":"1264","observed_at":"2025-11-01 11:49:36.359151+00:00","record_version_id":"d02f744a95372c1f89274e8f7d37507ae835b72c312f08fd182045896c917e85","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-7ab7c5e783fc8ebef8b8ee8cea019ee37a72ce74","semester":"1262","observed_at":"2025-09-28 04:58:07.340658+00:00","record_version_id":"054d6c807e1e03f8837904ad2286862a2002739459a81fe3fb1a230bc0b89f73","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-7b2f22d83ea05055ab90dcb77dc802382cccc6f7","semester":"1262","observed_at":"2025-05-21 17:56:46.096741+00:00","record_version_id":"b1de42c3726995fbc4551f1c627a48dbd3dbb1cb23da8e67358bdfd6a4563a79","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-7ca1175502be18eb51f7b016818a884a43fd4ebb","semester":"1262","observed_at":"2025-07-29 06:28:23.644795+00:00","record_version_id":"022a4462e6c9825f9fb41753c0a17d57c8be2565b94485e6f50dbf97fcb85741","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-7d8cee5e42602ad9ba5dc0322085af2fa0dffaac","semester":"1262","observed_at":"2025-05-22 01:15:19.291664+00:00","record_version_id":"6843f9cf271778c7b8faaf21c224bdb49be3bf2b8638c000181153285230669c","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-81a0d6a7f668c98c59bd05fa988aa079c5ad1f68","semester":"1262","observed_at":"2025-05-31 07:49:50.419452+00:00","record_version_id":"6a950cb7e1dc77c47c8070cc61f96748da4fa214c6b797bb0934e0619a6d90d3","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-82f31c08c746a214872494cde6ecaeb9330441b4","semester":"1262","observed_at":"2025-06-17 21:26:08.547156+00:00","record_version_id":"e45bcf867160a34b24b6af5b78a71b66427478c3d2179d0d19351ad4c4682095","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-84c248343c5a99ab42c5616c594812a5e3e31cbb","semester":"1262","observed_at":"2025-07-04 06:00:07.487827+00:00","record_version_id":"944b0210cc8efd174a10e2feb70830bfc2ed997de7f0d90c94c9f6288d497938","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-86334e7275e315853287e8580c35456c68df800b","semester":"1262","observed_at":"2025-05-22 06:53:03.336354+00:00","record_version_id":"ca0d6b2c4d95afe069b4380a507800fcf15ce12be669075a69390c7b749ba2b2","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-8686dc1664f93a6c05f50321410961b39b12f91b","semester":"1262","observed_at":"2025-05-28 10:17:41.595894+00:00","record_version_id":"d686d9b7d3f85a375f45b7b4bdf849ffaa67240d84c487e3a6293b22d819e332","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-86d941366886c9a14a2581c04e20d0bdd25e1b02","semester":"1262","observed_at":"2025-08-31 04:54:14.656039+00:00","record_version_id":"32f373935d98cc27d0ffc1ddcb44593ada9245a0ecbbc8cb69fa2e9a3e7311e8","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-8ba1d2e6cfbb1525b4f9f769ec164fb6ad0f237a","semester":"1262","observed_at":"2025-06-02 05:22:45.010398+00:00","record_version_id":"bfd44651db74e3abb7b26054778031df410cae3c4757dba9e3ce39b394a59a18","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-90e5aa456689a3b222016c05c319bbcd0bddd453","semester":"1262","observed_at":"2025-05-25 02:00:10.037905+00:00","record_version_id":"a55ffd836ff955e2965d82fdee02f3b9c6801b1f9445777b541ae0b4306ed2d9","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-92d960aa7e4cd6e4c9ac8b2e232092904038e9e4","semester":"1262","observed_at":"2025-06-08 01:50:46.451351+00:00","record_version_id":"2deadc89f6a59243bc050f3b0664108da824f4caa19cc61bad2ab09cfbeb9a54","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-94002ae588a8f71eca6d64105f2e603e8587293b","semester":"1262","observed_at":"2025-05-21 09:01:16.021508+00:00","record_version_id":"a1589ebf0dbf4b9574a2cf73bf5678d9147d2c299949cbc87aad6b3ccf40ecd4","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-9dd575e013c5c6e000ab3ad2d1288cc1dd9c96dc","semester":"1264","observed_at":"2025-12-21 04:47:46.589081+00:00","record_version_id":"1f985591c943e80d8bcfc51d9ec19ec59e12c48dab056453a0acaea3fdc7aeb4","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-a1ea26ed78cbd6e94bc3221e50a6ed9b6bf43871","semester":"1262","observed_at":"2025-05-25 09:16:55.180581+00:00","record_version_id":"64537767a83fb2ec231c6428b498b2d9294a1693498898185de488f376692a77","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-a76a3505853d5ea9c13bb98472b87361d469e2ee","semester":"1262","observed_at":"2025-05-28 18:03:41.793846+00:00","record_version_id":"e8d2a84031b0e3cedfaf2132eb88e7e874db1ca80cad25fd7d3bbbce0ff29127","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-b285d591f6acf3aa1910e02edc297664db86eb8c","semester":"1262","observed_at":"2025-06-10 22:48:22.460295+00:00","record_version_id":"8086718443b9a3e6ee9ae2da3e69f77d2a6bff0b6d3f2e642438002fe3c35204","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-b79af756e4f00ba98231feb859ed8e98239eb811","semester":"1262","observed_at":"2025-05-22 02:47:56.931581+00:00","record_version_id":"217689d3c822c93774dbba437221c81d62451602ae577623c5492b4f277d748f","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-b89e05b94730c6471439082d9279c0a834a4aeec","semester":"1262","observed_at":"2025-08-26 20:25:42.325239+00:00","record_version_id":"0a17e1f6494a646b4efe297a63f07e1114caa46094e0d027d5e1c112af6d0a71","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-b9279a12d1f2688df9e556183c94d41a89e431dc","semester":"1262","observed_at":"2025-07-06 01:49:47.203835+00:00","record_version_id":"12b0f73965bcdf54f0778d878031b7f4f83378133ab7de9ffdece872b948d99a","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-bbdc905e96cc681a5e9a8b02c795b682206e36d1","semester":"1262","observed_at":"2025-06-25 08:02:45.445730+00:00","record_version_id":"34b4203f286f740ecd94cfc37208b00917f77c0b06619c6190b1632067668306","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-bde225b5243a13d81827aa173e39c78a652b54a0","semester":"1262","observed_at":"2025-05-23 07:22:33.219345+00:00","record_version_id":"243a47e522dcc7b8e4654ef537244994b9ecc5c387fc47999ee6d82b17df4277","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-c1fac4cbfde737f4f4ac02cdf15e6f5a9b5b676d","semester":"1262","observed_at":"2025-05-22 01:15:19.291664+00:00","record_version_id":"6843f9cf271778c7b8faaf21c224bdb49be3bf2b8638c000181153285230669c","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-c6b62be45f8a13cba109ef2a7741b9cd9a6b8272","semester":"1262","observed_at":"2025-08-24 08:48:09.035112+00:00","record_version_id":"cfabd92ed549503f69ae06d3b1b559107f6cbb48b0b9dc55ecc6e74ce3b4432a","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-cc83f62cb6db61f39320916d819ad48ce55cd96d","semester":"1262","observed_at":"2025-05-22 00:45:35.735230+00:00","record_version_id":"3530122aa86ba824ab3cf11956a970f2557d8c04711bdeac848e79a5942e3dea","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-d33818a01b6b4c5e2902e704da6e5b713742d4dc","semester":"1262","observed_at":"2025-10-13 10:46:24.251475+00:00","record_version_id":"6abf0e146935841993bb876a8a70801b0f6d72f76ad9228c70b65c300a0e3c4e","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-d3c17db9832d3064916046caf891dd23f10b1bb9","semester":"1262","observed_at":"2025-08-26 11:30:42.288639+00:00","record_version_id":"89c5bd8d734a5e812a857df5e421fe5179d870027c64f9c6eb1f1415078bfe3b","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-d4ee226dc44268e9b6767fb9f5f20343322f494b","semester":"1262","observed_at":"2025-06-15 01:52:23.911697+00:00","record_version_id":"a58104be449b361cbf64c146a29b625edac580adb9544f14da47a32b0c258615","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-d8f9fc7f136e2033556d9d65bbdbdf33425dbdc9","semester":"1262","observed_at":"2025-05-28 16:48:12.400438+00:00","record_version_id":"c077ce7a31bc2c220b61a11d1517005d783c2ac94d77605c69075632142b0b3d","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-dbbc9739c616c3846b2e796e00a36fa41225f7d8","semester":"1262","observed_at":"2025-06-01 17:47:19.325144+00:00","record_version_id":"60c1c9c389c7537938aeb7d56ae4f0157b7fcfd1123faf6c8154801a2cfe39aa","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-dbe5f2d24b48c3177297a3d69c553cc9d1194bac","semester":"1262","observed_at":"2025-09-14 05:00:27.366070+00:00","record_version_id":"24e0a7878f08374f74a5a1bc30092226166e3714347970b6ef57f053a6500d78","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-dd357dc2758e9e282c30988a527ce37de92593b9","semester":"1264","observed_at":"2025-10-19 01:39:41.951404+00:00","record_version_id":"6685a88173f3213c4095648481a2c5c37f881a5eb38b8e360daef5b0b73502eb","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-de74de559e409a3fe668685e28043b888d2e6841","semester":"1262","observed_at":"2025-06-27 07:38:50.882897+00:00","record_version_id":"6e356171571c0717fe0c6af2b7e8cacb47f9a5284499c5911b29392328d04d38","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-deca7188cf536ade3203cd88b52a45e92a059617","semester":"1262","observed_at":"2025-06-27 00:37:46.087071+00:00","record_version_id":"8f24a3a9ae6e6b718c194ca7ff836c363aa9ff4ef15efc656178825aaad0d330","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-df60fa9ac3931c5612b0fa641b7a6f5fbec86fab","semester":"1262","observed_at":"2025-05-24 06:36:37.554678+00:00","record_version_id":"2cf9c472bda00595bb373564ddb9df1b64f822e47bf7d94ab7b3a2596e331f8f","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-e339da2d849aaf8bb4dceca07a84728af23cf755","semester":"1262","observed_at":"2025-07-23 08:49:22.748925+00:00","record_version_id":"9d649465b91328cc814156fb483a9c99ccb1e13f9b2d5548f9409255a7d68136","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-e934b1784835f5d36ae9b7ef0992b11e72c39142","semester":"1262","observed_at":"2025-06-01 06:21:25.219533+00:00","record_version_id":"b69a095c0293bf818ab52c9c61568d5fb744903f4436379210f4c8ef49a89224","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-ef471690c082448deebd2687eab84b0b78813c36","semester":"1262","observed_at":"2025-06-29 01:47:56.317645+00:00","record_version_id":"328779bfba32b4c16acdee5191de7302de75c318b09fc0e423796a5f66ad8eea","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-efc4e8e2cd7c3f68e059a308990c00c77c7611eb","semester":"1264","observed_at":"2025-10-16 05:48:13.935141+00:00","record_version_id":"296959bb33bb342df142904137563758b4278139285c6811768de5a231dae3de","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"},{"run_id":"legacy-f18b7749725097d41f69c60779ff38cf073b93b3","semester":"1262","observed_at":"2025-05-21 19:52:03.259353+00:00","record_version_id":"5b8cda80b16f018f322a42e9d1d77be5a400de1f7b2af96de80da647eb08363b","course_id":"CHEM 361","course_uid":"course_63f398e50e0bc4980d5d168f","catalog_version_id":"5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4","course_number":361,"subjects":["CHEM"],"title":"MACHINE LEARNING IN CHEMISTRY","description":"An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery.","requirements_text":"(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)"}]