STATISTICAL METHODS FOR BIOSCIENCE I covers statistical inference, ANOVA, regression, and diagnostics for bioscience applications.
Offering recorded · Fall 2026
Historical reviews of Bret Larget, Debraj Das: Exams can be difficult and heavily weighted, with one reviewer noting they covered unseen material, while another found them fair and focused on required knowledge.
Covers pattern classification, regression, clustering, and dimensionality reduction with a focus on statistical evaluation and Python implementation.
Offering recorded · Fall 2026
Historical reviews of John Gillett, Sebastian Raschka: Workload is generally manageable with recorded lectures, though Gillett's exams can be difficult and require careful review, while one review cites unorganized materials and confusing group policies.
Introduction to deep learning and generative models connecting neural networks to statistical concepts.
Offering recorded · Fall 2026
Historical reviews of Yiqiao Zhong: Reviewers note low overall workload, though exams can be confusing if lecture concepts are not understood. One student found the difficulty manageable with effort, while another cited flexible deadlines and fine tests.
STAT 479 covers special topics in statistics of interest to undergraduate students.
Offering recorded · Fall 2026
Historical reviews of John Gillett, Karl Rohe, Kris Sankaran: Workload varies by instructor; Gillett’s course required independent Linux learning and debugging, while Sankaran’s recent term involved graduate-level assignments. Sankaran’s earlier terms featured spread-out homeworks, and Rohe provided few guidelines.