[{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-49752a259f17fab0f49665b3","output_id":"484193b71e97ca3ea0cd0cab2d86eb029ca459e6a9636041ff38529b519c8cbe","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"(ACT SCI 640,GEN BUS 656,STAT 333, or340), or declared in undergraduate Business Exchange program\",\"id\":\"root\",\"kind\":\"any\"},{\"children\":[\"c1\",\"c2\",\"c3\",\"c4\"],\"condition\":null,\"course\":null,\"evidence\":\"ACT SCI 640,GEN BUS 656,STAT 333, or340\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":\"declared in undergraduate Business Exchange program\",\"course\":null,\"evidence\":\"declared in undergraduate Business Exchange program\",\"id\":\"n2\",\"kind\":\"condition\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":640,\"minimum_grade\":null,\"subjects\":[\"ACTSCI\"],\"timing\":\"prior\"},\"evidence\":\"ACT SCI 640\",\"id\":\"c1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":656,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 656\",\"id\":\"c2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"c3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"or340\",\"id\":\"c4\",\"kind\":\"course\"}],\"notes\":[\"Parsed 'or340' as STAT 340 based on linked courses and subject inheritance from STAT 333.\",\"Treated comma-separated list within parentheses as an OR group based on explicit 'or' before 340 and standard catalog conventions for alternative prerequisites.\"],\"root\":\"root\",\"status\":\"needs_review\"}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":true},{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-49752a259f17fab0f49665b3","output_id":"484193b71e97ca3ea0cd0cab2d86eb029ca459e6a9636041ff38529b519c8cbe","section":"search_profile","status":"valid","value_json":"{\"activity_tags\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":1,\"quote\":\"Build predictive models for the purpose of probabilistic forecasting in business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"label\":\"data-analysis\"}],\"assumed_background\":[],\"search_phrases\":[\"probabilistic forecasting business\",\"risk analytics actuarial science\",\"machine learning distribution regression\",\"heavy-tailed regression count data\",\"survival data analysis NLP\",\"feature engineering neural networks\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Modeling, communicating, and managing risk and uncertainty\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":1,\"quote\":\"Build predictive models for the purpose of probabilistic forecasting in business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Building predictive models for probabilistic forecasting\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":3,\"quote\":\"Perform feature engineering to incorporate complex pattern and unstructured data into the predictive model development for business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Feature engineering for complex patterns and unstructured data\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":4,\"quote\":\"Interpret the prediction outcome and communicate results to non-technical business audiences\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Interpreting predictions and communicating results to non-technical audiences\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"title\",\"quote\":\"RISK ANALYTICS\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications. Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Risk Analytics develops tools for modeling and managing business risk using probabilistic forecasting, integrating machine learning with distribution-based regression.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Heavy-tailed regression, count data regression, and survival data analysis\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"feature engineering using neural networks and natural language processing\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Feature engineering using neural networks and natural language processing\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Emphasis on the notation of probabilistic forecasting\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Probabilistic forecasting notation\"}]}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":true},{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-49752a259f17fab0f49665b3","output_id":"484193b71e97ca3ea0cd0cab2d86eb029ca459e6a9636041ff38529b519c8cbe","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":true},{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-49752a259f17fab0f49665b3","output_id":"484193b71e97ca3ea0cd0cab2d86eb029ca459e6a9636041ff38529b519c8cbe","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"4365de2af0e4b866b57dd3cdd2920823b1c3a78d33f52efe28af488392d9a896\",\"course_id\":\"ACTSCI 657\",\"current_instructors\":[],\"difficulty_workload\":[],\"errors\":[],\"historical_context\":[],\"message\":\"No course-specific reviews available\",\"offered\":false,\"profile_hash\":\"3f5110430805ccdd533c0c072df62d68a8a5f5441c4530bbdb15d419a199a09c\",\"quick_take\":[{\"citations\":[{\"course_id\":\"ACTSCI 657\",\"run_id\":\"20261014T203208-0851c9a1\",\"source_record\":{\"entity_id\":\"5433eabc-d4b9-32a7-8cf3-db74effcc61d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1244\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 657\",\"run_id\":\"20261014T203208-0851c9a1\",\"source_record\":{\"entity_id\":\"5433eabc-d4b9-32a7-8cf3-db74effcc61d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 657\",\"run_id\":\"20261014T203208-0851c9a1\",\"source_record\":{\"entity_id\":\"5433eabc-d4b9-32a7-8cf3-db74effcc61d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2024: 3.39 GPA, 60.9% A/AB (n=23 letter grades); Spring 2025: 3.60 GPA, 73.5% A/AB (n=34 letter grades); Spring 2026: 3.34 GPA, 59.1% A/AB (n=22 letter grades).\"}],\"student_experience\":[],\"task_hash\":\"9dea82ef7f2113a5605c1c6030c4f9b19967eb742cc93a78268fb92357d29f5c\",\"teaching_history\":[],\"term_id\":\"1274\",\"term_name\":\"2027 Sprng\",\"version\":3}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":true},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"4952917668ce05672fdb0f91b5c610689434f30ff55ee787fd54a0580c4f896f","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n0\",\"n1\"],\"condition\":null,\"course\":null,\"evidence\":\"(ACT SCI 640,GEN BUS 656,STAT 333, or340), or declared in undergraduate Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":640,\"minimum_grade\":null,\"subjects\":[\"ACTSCI\"],\"timing\":\"prior\"},\"evidence\":\"ACT SCI 640\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":656,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 656\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"or340\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"declared in undergraduate Business Exchange program\",\"course\":null,\"evidence\":\"declared in undergraduate Business Exchange program\",\"id\":\"n5\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","error":"Node n0 references itself; remove the self-reference.\nCycle reaches node n0; requirement graphs must be trees.\nUnreachable nodes: n2, n3, n4, n5; connect all conditions and exclusions to the root.","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"4952917668ce05672fdb0f91b5c610689434f30ff55ee787fd54a0580c4f896f","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Linear regression and prediction\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Probability, statistical inference, and R programming\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Statistical learning and risk modeling\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Predictive modeling foundations and business applications\"}],\"search_phrases\":[\"risk analytics\",\"probabilistic forecasting\",\"heavy-tailed regression\",\"survival data analysis\",\"neural networks NLP feature engineering\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"Modeling, communicating, and managing risk and uncertainty\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models.\"}],\"text\":\"Probabilistic forecasting and integrated predictive modeling\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Advanced regression techniques and NLP-based feature engineering\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"title\",\"quote\":\"RISK ANALYTICS\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"RISK ANALYTICS teaches modeling and managing business risk using probabilistic forecasting and machine learning-integrated regression.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Heavy-tailed, count, and survival data regression\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"feature engineering using neural networks and natural language processing\"}],\"text\":\"Neural networks and natural language processing for feature engineering\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"4952917668ce05672fdb0f91b5c610689434f30ff55ee787fd54a0580c4f896f","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"e50bf14a8e99e8c254828285beda27f6d036c90c948eb1f8326ddfc9d06af9f2","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\"],\"condition\":null,\"course\":null,\"evidence\":\"(ACT SCI 640,GEN BUS 656,STAT 333, or340), or declared in undergraduate Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":640,\"minimum_grade\":null,\"subjects\":[\"ACTSCI\"],\"timing\":\"prior\"},\"evidence\":\"ACT SCI 640\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":656,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 656\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"or340\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"declared in undergraduate Business Exchange program\",\"course\":null,\"evidence\":\"declared in undergraduate Business Exchange program\",\"id\":\"n5\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"e50bf14a8e99e8c254828285beda27f6d036c90c948eb1f8326ddfc9d06af9f2","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Linear regression and prediction\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Probability, statistical inference, and R programming\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Statistical learning and risk modeling\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Predictive modeling foundations and business applications\"}],\"search_phrases\":[\"risk analytics\",\"probabilistic forecasting\",\"heavy-tailed regression\",\"survival data analysis\",\"neural networks NLP feature engineering\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"Modeling, communicating, and managing risk and uncertainty\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models.\"}],\"text\":\"Probabilistic forecasting and integrated predictive modeling\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Advanced regression techniques and NLP-based feature engineering\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"title\",\"quote\":\"RISK ANALYTICS\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"RISK ANALYTICS teaches modeling and managing business risk using probabilistic forecasting and machine learning-integrated regression.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Heavy-tailed, count, and survival data regression\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"feature engineering using neural networks and natural language processing\"}],\"text\":\"Neural networks and natural language processing for feature engineering\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"e50bf14a8e99e8c254828285beda27f6d036c90c948eb1f8326ddfc9d06af9f2","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-6556a505c10f448d98d02466","output_id":"80ce0fd84e0b0c4d59dc33ef3027f2645f8b7c09765d8a9e74eb917ec0bacf37","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"(ACT SCI 640,GEN BUS 656,STAT 333, or340), or declared in undergraduate Business Exchange program\",\"id\":\"root\",\"kind\":\"any\"},{\"children\":[\"c1\",\"c2\",\"c3\",\"c4\"],\"condition\":null,\"course\":null,\"evidence\":\"ACT SCI 640,GEN BUS 656,STAT 333, or340\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":\"declared in undergraduate Business Exchange program\",\"course\":null,\"evidence\":\"declared in undergraduate Business Exchange program\",\"id\":\"n2\",\"kind\":\"condition\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":640,\"minimum_grade\":null,\"subjects\":[\"ACTSCI\"],\"timing\":\"prior\"},\"evidence\":\"ACT SCI 640\",\"id\":\"c1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":656,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 656\",\"id\":\"c2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"c3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"or340\",\"id\":\"c4\",\"kind\":\"course\"}],\"notes\":[\"Parsed 'or340' as STAT 340 based on linked courses and subject inheritance from STAT 333.\",\"Treated comma-separated list within parentheses as an OR group based on explicit 'or' before 340 and standard catalog conventions for alternative prerequisites.\"],\"root\":\"root\",\"status\":\"needs_review\"}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":false},{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-6556a505c10f448d98d02466","output_id":"80ce0fd84e0b0c4d59dc33ef3027f2645f8b7c09765d8a9e74eb917ec0bacf37","section":"search_profile","status":"valid","value_json":"{\"activity_tags\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":1,\"quote\":\"Build predictive models for the purpose of probabilistic forecasting in business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"label\":\"data-analysis\"}],\"assumed_background\":[],\"search_phrases\":[\"probabilistic forecasting business\",\"risk analytics actuarial science\",\"machine learning distribution regression\",\"heavy-tailed regression count data\",\"survival data analysis NLP\",\"feature engineering neural networks\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Modeling, communicating, and managing risk and uncertainty\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":1,\"quote\":\"Build predictive models for the purpose of probabilistic forecasting in business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Building predictive models for probabilistic forecasting\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":3,\"quote\":\"Perform feature engineering to incorporate complex pattern and unstructured data into the predictive model development for business applications.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Feature engineering for complex patterns and unstructured data\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"official_learning_outcomes\",\"outcome_index\":4,\"quote\":\"Interpret the prediction outcome and communicate results to non-technical business audiences\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Interpreting predictions and communicating results to non-technical audiences\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"title\",\"quote\":\"RISK ANALYTICS\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications. Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Risk Analytics develops tools for modeling and managing business risk using probabilistic forecasting, integrating machine learning with distribution-based regression.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Heavy-tailed regression, count data regression, and survival data analysis\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"feature engineering using neural networks and natural language processing\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Feature engineering using neural networks and natural language processing\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Emphasis on the notation of probabilistic forecasting\",\"source_url\":\"https://guide.wisc.edu/courses/act_sci/\"}],\"text\":\"Probabilistic forecasting notation\"}]}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":false},{"run_id":"20261014T203208-0851c9a1","observed_at":"2026-10-14 17:50:09.728220+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"fae7d45cc35a6aafe9a2d98f4e898afb442d68cca4f0fccaa51c3adc918e5449","record_version_id":"ae67e7f397257873e05005ab3622d9bd1f20a024ae9d3a702fa5e58156513fe8","job_id":"enrich-6556a505c10f448d98d02466","output_id":"80ce0fd84e0b0c4d59dc33ef3027f2645f8b7c09765d8a9e74eb917ec0bacf37","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"Qwen/Qwen3.8-27B","model_revision":"1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-789789da373eecc1ff75f626","output_id":"cb17e4dada0d85fdbfe49b6a2caadb90173d12c347d3ba519ef768f108279f1f","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\"],\"condition\":null,\"course\":null,\"evidence\":\"(ACT SCI 640,GEN BUS 656,STAT 333, or340), or declared in undergraduate Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":640,\"minimum_grade\":null,\"subjects\":[\"ACTSCI\"],\"timing\":\"prior\"},\"evidence\":\"ACT SCI 640\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":656,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 656\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"or340\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"declared in undergraduate Business Exchange program\",\"course\":null,\"evidence\":\"declared in undergraduate Business Exchange program\",\"id\":\"n5\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-789789da373eecc1ff75f626","output_id":"cb17e4dada0d85fdbfe49b6a2caadb90173d12c347d3ba519ef768f108279f1f","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Linear regression and prediction\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Probability, statistical inference, and R programming\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Statistical learning and risk modeling\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Predictive modeling foundations and business applications\"}],\"search_phrases\":[\"risk analytics\",\"probabilistic forecasting\",\"heavy-tailed regression\",\"survival data analysis\",\"neural networks NLP feature engineering\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"Modeling, communicating, and managing risk and uncertainty\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models.\"}],\"text\":\"Probabilistic forecasting and integrated predictive modeling\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Advanced regression techniques and NLP-based feature engineering\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"title\",\"quote\":\"RISK ANALYTICS\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"RISK ANALYTICS teaches modeling and managing business risk using probabilistic forecasting and machine learning-integrated regression.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Heavy-tailed, count, and survival data regression\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"feature engineering using neural networks and natural language processing\"}],\"text\":\"Neural networks and natural language processing for feature engineering\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-789789da373eecc1ff75f626","output_id":"cb17e4dada0d85fdbfe49b6a2caadb90173d12c347d3ba519ef768f108279f1f","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"0a717534f30636cd0ce2dfa857d57b9cd6859473d48d7360337dd0f033b6386a","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\"],\"condition\":null,\"course\":null,\"evidence\":\"(ACT SCI 640,GEN BUS 656,STAT 333, or340), or declared in undergraduate Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":640,\"minimum_grade\":null,\"subjects\":[\"ACTSCI\"],\"timing\":\"prior\"},\"evidence\":\"ACT SCI 640\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":656,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 656\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"or340\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"declared in undergraduate Business Exchange program\",\"course\":null,\"evidence\":\"declared in undergraduate Business Exchange program\",\"id\":\"n5\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"0a717534f30636cd0ce2dfa857d57b9cd6859473d48d7360337dd0f033b6386a","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Linear regression and prediction\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Probability, statistical inference, and R programming\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Statistical learning and risk modeling\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Predictive modeling foundations and business applications\"}],\"search_phrases\":[\"risk analytics\",\"probabilistic forecasting\",\"heavy-tailed regression\",\"survival data analysis\",\"neural networks NLP feature engineering\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"Modeling, communicating, and managing risk and uncertainty\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models.\"}],\"text\":\"Probabilistic forecasting and integrated predictive modeling\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Advanced regression techniques and NLP-based feature engineering\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"title\",\"quote\":\"RISK ANALYTICS\"},{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications.\"}],\"text\":\"RISK ANALYTICS teaches modeling and managing business risk using probabilistic forecasting and machine learning-integrated regression.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.\"}],\"text\":\"Heavy-tailed, count, and survival data regression\"},{\"evidence\":[{\"course_id\":\"ACTSCI 657\",\"field\":\"description\",\"quote\":\"feature engineering using neural networks and natural language processing\"}],\"text\":\"Neural networks and natural language processing for feature engineering\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"0a717534f30636cd0ce2dfa857d57b9cd6859473d48d7360337dd0f033b6386a","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_682cc0b6db60ff7e894efa36","course_id":"ACTSCI 657","catalog_version_id":"ae64867cf2ab9fc07eb2e4f1eb8ce93daf0c598655510121150589ddc524d772","record_version_id":"dce623e7e4fa649a9fdbc3f4347beb87fa57f111a31a49c8368128752ebecab7","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"0a717534f30636cd0ce2dfa857d57b9cd6859473d48d7360337dd0f033b6386a","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"afbad7bc68058c9d5cb90830c61252f683483d44e4de73871e64b4c824ad8eee\",\"course_id\":\"ACTSCI 657\",\"current_instructors\":[],\"difficulty_workload\":[],\"errors\":[],\"historical_context\":[],\"message\":\"No course-specific reviews available\",\"offered\":false,\"profile_hash\":\"672f506f2fc2f46a071b9777f4a92cc197b2ccdeef25590e9285146d8c7e7f90\",\"quick_take\":[{\"citations\":[{\"course_id\":\"ACTSCI 657\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"5433eabc-d4b9-32a7-8cf3-db74effcc61d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1244\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 657\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"5433eabc-d4b9-32a7-8cf3-db74effcc61d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 657\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"5433eabc-d4b9-32a7-8cf3-db74effcc61d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2024: 3.39 GPA, 60.9% A/AB (n=23 letter grades); Spring 2025: 3.60 GPA, 73.5% A/AB (n=34 letter grades); Spring 2026: 3.34 GPA, 59.1% A/AB (n=22 letter grades).\"}],\"student_experience\":[],\"task_hash\":\"74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68\",\"teaching_history\":[],\"term_id\":\"1272\",\"term_name\":\"2026 Fall\",\"version\":2}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false}]