[{"run_id":"20260906T231458-5fdd2fff","semester":"1272","observed_at":"2026-09-06 23:14:58.172943+00:00","record_version_id":"15080f3259226ee69160969fe9ace53d1a02ff09ae0b68d41fa4beb1d756d71e","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"20260907T155543-ce3781c4","semester":"1272","observed_at":"2026-09-07 15:55:43.033547+00:00","record_version_id":"15080f3259226ee69160969fe9ace53d1a02ff09ae0b68d41fa4beb1d756d71e","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-08c92e359bbbc110b0778b7e0b15d6f40000e6b1","semester":"1262","observed_at":"2025-08-21 07:05:14.730696+00:00","record_version_id":"f3bd7e681b72e54092fafdbac4d8ecb16a6e79bbd68d0645e887804d02659594","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-0a7f4bce29c816ef4d3d71b4eb8a2a3e15e33cc1","semester":"1262","observed_at":"2025-06-18 20:04:02.695444+00:00","record_version_id":"d4a6a6fdbbd57c7499229358ec8fe8eb83dccd6e6d158ea2641670f5e553a380","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-0cb4d29a588ec7f11c7e00b7cf5dc3a0d23d0349","semester":"1266","observed_at":"2026-01-22 01:00:26.555427+00:00","record_version_id":"f0c4c1cb5628567224e7c7e1b9c162afe3108d61bab701b7610a23935e39aba7","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-11c30ec39e91498cbfcf3a923c53a4b03e387280","semester":"1262","observed_at":"2025-07-20 04:58:26.847117+00:00","record_version_id":"15bc1340798e1831cb4b71636bb4bf6386df6688685c563ff8fe2a322d27cb07","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-13cd500edf2587e9a3428f326253738fbea965ea","semester":"1264","observed_at":"2025-12-14 04:50:04.653658+00:00","record_version_id":"188fe18a32e93fa7b6a0eac3dee6f3d7d3741a212f295e2a9a41386b67ac6f00","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-1da08b2de39f05e6bc877c189fc2fd6eeda563f5","semester":"1262","observed_at":"2025-07-01 00:03:56.707251+00:00","record_version_id":"0a4f8ddc76b6609291ce42e70ff3b0f7b000b72bbc51406615911f8217a75aee","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-2031af47efe133b0e7877f433286ff07385afc41","semester":"1262","observed_at":"2025-07-02 20:16:45.749584+00:00","record_version_id":"2d6075b32f12437c9eb9db78c24f8531ce831f1653dc1ef96cdbdfb61ab52d3a","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-238eba6f32fbc30407f616dd0628ff02a2a824dd","semester":"1262","observed_at":"2025-08-17 05:15:07.788352+00:00","record_version_id":"c084560da7bbd05ec461df9d8545d11783bf5070ad108676add390f2593c2d41","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-2923f91826ce253b7aeefa9f75b4a39fe4c80ff0","semester":"1262","observed_at":"2025-08-10 05:10:25.938416+00:00","record_version_id":"c86965b79fab2c92d745687a8c4c6a5991bcc62eb11315bd6706f5871d75e0b7","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-2a05214a4023e0157fa6f7d188c85308f714176c","semester":"1262","observed_at":"2025-09-07 04:56:38.255315+00:00","record_version_id":"9fb5633898361248239584365d3a1b7875360222a6ba76f028d32791a1f4b251","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-33a1225644e5a5855d60f66e65413946e72fedf4","semester":"1262","observed_at":"2025-06-22 01:52:05.152650+00:00","record_version_id":"98bf2ec9ed93a9f626875dec8f8715a9449dd7b02527fb676c1c9203ab55935a","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-3b89c83dbf5208e13756184de1f2b2d0c09af35f","semester":"1262","observed_at":"2025-06-30 06:40:10.235522+00:00","record_version_id":"f3127efc936aa7e0d413c79e7bb3f73bf27690b1957eac8d42aa5b94137ec430","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-486f091e051fdb4ccdb80ee7b4fb0a5814510324","semester":"1264","observed_at":"2025-11-16 05:32:31.273726+00:00","record_version_id":"3c33c9b491d529e87c68b608caf4e5ea8b77668db0135808612231be97ff0686","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-50393cfc5fd5598d4f4731ef1919f2ff53af07d9","semester":"1262","observed_at":"2025-06-30 06:05:57.438846+00:00","record_version_id":"048d268569ba362c4483afb1cb718401692f7141ff091410dd61b1b76a8d3854","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-50f6947786b208c800db8bbd0cec84a8f0f187d2","semester":"1262","observed_at":"2025-06-02 03:49:42.915428+00:00","record_version_id":"29209bf1a9f7dc25ab280994968ed6646aaa81ceac14253c2d5a18b5990815d9","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-51d86ed4082dced3803c295419b0ec8440942016","semester":"1264","observed_at":"2025-11-12 04:50:41.141307+00:00","record_version_id":"5095e9ceab3eee002479b5d56250b7c819308f38cefe1d76e082d5ccbbcf3013","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-53101bd1c10db0d00d094e4c9154d4c244ff23d9","semester":"1262","observed_at":"2025-10-05 04:58:05.366968+00:00","record_version_id":"4f7070d6ce7199f788d669766269a88fd559e6e18fefcda6436abe8fd0c0feb1","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-60949965544fce098cc006354e201149005fb77b","semester":"1264","observed_at":"2025-10-26 05:07:14.077864+00:00","record_version_id":"d94ee07b544d0f4aad990673af3abb0b5b9e7bf45b4e22d2cd65152fb01585ef","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-6885b56099f55e08aa0205546369fb0381d50e13","semester":"1262","observed_at":"2025-07-13 04:59:22.367394+00:00","record_version_id":"b9cc4762e798f4ea77a185e4140cfa64e99a6bb99681a340c6ee824272be14ce","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-69d4b2b16e8268f75b6390b3a0e2ce8bf73999da","semester":"1262","observed_at":"2025-06-30 03:21:29.925498+00:00","record_version_id":"6d2716ba3d070ca225a5b2971e56d6af83338026160b4a2c924d18d2bed74abb","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-6b29c3e0673b2f9a736d2363db5409e03d5b3ed4","semester":"1264","observed_at":"2025-12-03 12:33:41.418072+00:00","record_version_id":"98284b8671c418c02839fccdad3f52ca3c56446debeda560f846236de8d537bf","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-73259984748de484976ef2ad56ad7ab3d4cb4518","semester":"1262","observed_at":"2025-06-26 08:27:56.461216+00:00","record_version_id":"0079aa2ff97327a19c502cadcbd5be8d5a34ed1606072539c6921d16eba23b8c","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-741602fbc6252c232a0b9e6bc1a9ae3101625f5a","semester":"1262","observed_at":"2025-06-30 22:49:32.522034+00:00","record_version_id":"34c35440a2f0ae16643ba51c174f8c557152c2472bdee23e995ce45606373a51","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-7774f45558276ec29bf626e58fffb4a27bdbb2a9","semester":"1264","observed_at":"2025-11-01 11:49:36.359151+00:00","record_version_id":"d1f753efb5add75ef8ac8c78df63ae28917a86fbef49ca63637dec8d1d266470","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-7ab7c5e783fc8ebef8b8ee8cea019ee37a72ce74","semester":"1262","observed_at":"2025-09-28 04:58:07.340658+00:00","record_version_id":"e4698e1a671d304e47242f4f258e158470b23d2b3a797643c0561e6e39342e05","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-7ca1175502be18eb51f7b016818a884a43fd4ebb","semester":"1262","observed_at":"2025-07-29 06:28:23.644795+00:00","record_version_id":"f3127efc936aa7e0d413c79e7bb3f73bf27690b1957eac8d42aa5b94137ec430","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-82f31c08c746a214872494cde6ecaeb9330441b4","semester":"1262","observed_at":"2025-06-17 21:26:08.547156+00:00","record_version_id":"b286fa1af4100211c9678e32b774e82641c891c1b53850facf19ef1ea28d8111","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-84c248343c5a99ab42c5616c594812a5e3e31cbb","semester":"1262","observed_at":"2025-07-04 06:00:07.487827+00:00","record_version_id":"aacd2782ca8369351f83b343d20a0cbe7e8f6d614d188f23e9b4991e99f0c027","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-86d941366886c9a14a2581c04e20d0bdd25e1b02","semester":"1262","observed_at":"2025-08-31 04:54:14.656039+00:00","record_version_id":"d771afd621f7dfae58904d216e6cb16aa46b2dd5aa86fb6af076727fa7fb669c","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-8ba1d2e6cfbb1525b4f9f769ec164fb6ad0f237a","semester":"1262","observed_at":"2025-06-02 05:22:45.010398+00:00","record_version_id":"43f663c554613b6b6cb8be4244772f31a5668f6126c4525b8d3ec5a5e921f348","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-92d960aa7e4cd6e4c9ac8b2e232092904038e9e4","semester":"1262","observed_at":"2025-06-08 01:50:46.451351+00:00","record_version_id":"05f8d49283825097b8bdee920fa8a4e418e74b4103d3b0163a451a7699187f9c","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-9dd575e013c5c6e000ab3ad2d1288cc1dd9c96dc","semester":"1264","observed_at":"2025-12-21 04:47:46.589081+00:00","record_version_id":"bd1559801672e74568fc6df5276492b317e786b016bb899cd7ef1ff6fe05c981","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-b285d591f6acf3aa1910e02edc297664db86eb8c","semester":"1262","observed_at":"2025-06-10 22:48:22.460295+00:00","record_version_id":"4af520ea01f5c37997f07a5ebfcb4db642343d23c63be8c28eb51c2d91793d15","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-b89e05b94730c6471439082d9279c0a834a4aeec","semester":"1262","observed_at":"2025-08-26 20:25:42.325239+00:00","record_version_id":"85fa034261f77839fe0d4d80cf2fc47fdb1c7003b12688e2caf013371470e06f","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-b9279a12d1f2688df9e556183c94d41a89e431dc","semester":"1262","observed_at":"2025-07-06 01:49:47.203835+00:00","record_version_id":"2fc08cb0d00da323ffd75e6ba86644c7dfbdaec885bcda3baafc95a18c2464d3","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-bbdc905e96cc681a5e9a8b02c795b682206e36d1","semester":"1262","observed_at":"2025-06-25 08:02:45.445730+00:00","record_version_id":"f776acabc8db5168e4ad4559885118596b22c3063791c4a6d87035d8a37025f2","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-c6b62be45f8a13cba109ef2a7741b9cd9a6b8272","semester":"1262","observed_at":"2025-08-24 08:48:09.035112+00:00","record_version_id":"5c53d75ccaebae8bb259f4c71793bccd13290c37c185d9d33d2f85eb80fb9aa8","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-d33818a01b6b4c5e2902e704da6e5b713742d4dc","semester":"1262","observed_at":"2025-10-13 10:46:24.251475+00:00","record_version_id":"30a995f08ac7be7e4b8e7d2615ea39d6f91a65ce40671248807975119e16124a","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-d3c17db9832d3064916046caf891dd23f10b1bb9","semester":"1262","observed_at":"2025-08-26 11:30:42.288639+00:00","record_version_id":"c55efd01a4cb077195ef26378d06b09287e12871eb9388f998020122d0dff13c","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-d4ee226dc44268e9b6767fb9f5f20343322f494b","semester":"1262","observed_at":"2025-06-15 01:52:23.911697+00:00","record_version_id":"628ee1c1b9e99359e25a465a92da2b98def306ec2b09f8010568b9b7cb41634b","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-dbe5f2d24b48c3177297a3d69c553cc9d1194bac","semester":"1262","observed_at":"2025-09-14 05:00:27.366070+00:00","record_version_id":"fef72ab887e62a36d3330ca853d4dcac5cc6b0393adcd70d4533194050508f46","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-dd357dc2758e9e282c30988a527ce37de92593b9","semester":"1264","observed_at":"2025-10-19 01:39:41.951404+00:00","record_version_id":"f86ae03d32e5583e77bae84721a131a4a50480a4fdcd9d5292be24d2ebe20b8c","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"},{"run_id":"legacy-de74de559e409a3fe668685e28043b888d2e6841","semester":"1262","observed_at":"2025-06-27 07:38:50.882897+00:00","record_version_id":"a88728ded1fc5bb537ce4f87aabe10a823edee61a96f519747b9719add4c7556","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-deca7188cf536ade3203cd88b52a45e92a059617","semester":"1262","observed_at":"2025-06-27 00:37:46.087071+00:00","record_version_id":"621223745439e3001f1a31c97860a8bd8e2b2d4869bff1e41963edbafbdfdcc8","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-e339da2d849aaf8bb4dceca07a84728af23cf755","semester":"1262","observed_at":"2025-07-23 08:49:22.748925+00:00","record_version_id":"108fa65f87aaf9620940f24f4e4cf6aa5dda5f0e8683ea31f6563db222703cc9","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-ef471690c082448deebd2687eab84b0b78813c36","semester":"1262","observed_at":"2025-06-29 01:47:56.317645+00:00","record_version_id":"030abae39ac37764e26d484bb113a7dbcbf1bd27a691aec2de88384a189d1ed3","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"9691a89b5eb9423fc0f26a086733dd63f94d46d533e91bc64b7b66b8db8db713","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"GEN BUS 656or concurrent enrollment"},{"run_id":"legacy-efc4e8e2cd7c3f68e059a308990c00c77c7611eb","semester":"1264","observed_at":"2025-10-16 05:48:13.935141+00:00","record_version_id":"33fee5f39cba37f922932f2d19fc5456e338621199c53fc9e8bac8f0c8715b88","course_id":"GENBUS 657","course_uid":"course_d867856b67bb1895c4ab83ea","catalog_version_id":"b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792","course_number":657,"subjects":["GENBUS"],"title":"MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS","description":"An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.","requirements_text":"ACT SCI 640or (GEN BUS 656or concurrent enrollment)"}]