This is a tentative schedule.

(Wed, 1/15) Lecture 1: Introduction [slides]
(Wed, 1/15) HW 0 Released [written] [coding]

(Wed, 1/22) Lecture 2: Linear Regression Part 1 [slides]

(Mon, 1/27) Lecture 3: Linear Regression Part 2 [slides]

(Wed, 1/29) Lecture 4: Linear Regression Part 3 [slides]

(Wed, 1/29) HW 0 Due (8pm), HW 1 Released [written] [coding]

(Mon, 2/3) Lecture 5: Logistic Regression Part 1 [slides]

(Wed, 2/5) Lecture 6: Logistic Regression Part 2 [slides]

Linear Regression Worksheet 1 and Video Walkthrough Released [Worksheet] [Dataset] [Walkthrough 1] [Walkthrough 2]

(Mon, 2/10) Lecture 7: Neural Networks Part 1 [slides]

(Wed, 2/12) Lecture 8: Neural Networks Part 2 [slides]

(Wed, 2/12) HW 1 Due (8pm), HW 2 Released [written] [coding]

(Mon, 2/17) Lecture 9: K Nearest Neighbors [slides]

(Wed, 2/19) Lecture 10: Decision Trees [slides]

(Mon, 2/24) Lecture 11: K Means Clustering [slides]

(Wed, 2/26) Lecture 12: Principal Components Analysis [slides]

(Mon, 3/3) Lecture 13: Review [slides]
(Mon, 3/3) HW 2 Due (8pm)

(Wed, 3/5) Lecture 14: Midterm 1
Practice Exam 1 [exam] [solutions]
Practice Exam 2 [exam] [solutions]

(Mon, 3/17) Lecture 15: Computer Vision Part 1 [slides]
(Mon, 3/17) Project Instructions Release [project]

(Wed, 3/19) Lecture 16: Computer Vision Part 2 [slides]
(Wed, 3/19) HW 3 Released [written] [coding]

(Mon, 3/24) Lecture 17: Natural Language Processing Part 1 [slides]

(Wed, 3/26) Lecture 18: Natural Language Processing Part 2 [slides]

(Mon, 3/31) Lecture 19: Reinforcement Learning Part 1 [slides]

(Wed, 4/2) Lecture 20: Reinforcement Learning Part 2 [slides]
(Wed, 4/2) HW 3 Due (8pm), HW 4 Released [written] [coding]

(Mon, 4/7) Lecture 21: Ensembles Part 1

(Wed, 4/9) Lecture 22: Ensembles Part 2

(Mon, 4/14) Lecture 23: Recommender Systems

(Wed, 4/16) Lecture 24: Robust Machine Learning
(Wed, 4/16) HW 4 Due (8pm)

(Mon, 4/21) Lecture 25: Generative AI

(Wed, 4/23) Lecture 25: Ethics

(Mon, 4/28) Lecture 26: Review

(Wed, 4/30) Lecture 27: Midterm 2