MW 4:10pm–5:25pm · MW 1:10pm–2:25pm
2 distinct times across 2 sections.
2 of 2 sections have room
Prerequisites Prerequisites: Optimization, applied probability, statistics, and knowledge of or experience in computer programming.
Description
MS IEOR students only. Introduction to machine learning, practical use of ML algorithms and applications to financial engineering and operations. Supervised learning: regression, classification, resampling methods, regularization, support vector machines (SVMs), and deep learning. Unsupervised learning: dimensionality reduction, matrix decomposition, and clustering algorithms
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Sections2
- 4 of 92 seats left
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
- 44 of 100 seats left
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
In your week
Workload and grading
- Grading
- Not published
- Format
- Not published
- Credits
- 3 credits
Reported workload
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