Principles of Machine Learning
Boston University - Spring 2024
Course staff and office hours
Instructor: Prof. Iddo Drori, Wednesday 4:15-5:15pm, CDS 839
Teaching Fellow: Ge Gao, TBD
Teaching Fellow: Ryan Yu, TBD
Textbooks
Machine Learning: A Probabilistic Approach, Murphey, MIT Press, 2022
First Day of Classes (Thursday, January 18)
Lecture 1 (Monday, January 22): Introduction
Lecture 2 (Wednesday, January 24): Regression
Lecture 3 (Monday, January 29): Gradient descent
Lecture 4 (Wednesday, January 31): Classifiers
Lecture 5 (Monday, February 5): Logistic regression
Lecture 6 (Wednesday, February 7): Feature representation
Lecture 7 (Monday, February 12): Neural networks
Lecture 8 (Wednesday, February 14): Neural networks
Presidents' Day Holiday (Monday, February 19) No classes
Lecture 9 (Wednesday, February 21): Convolutional neural networks
Lecture 10 (Monday, February 26): Convolutional neural networks
Lecture 11 (Wednesday, February 28): Sequence models
Lecture 12 (Monday, March 4): Recurrent neural networks
Lecture 13 (Wednesday, March 6): Autoencoders
Spring Recess (March 9 - March 17)
Lecture 14 (Monday, March 18): Diffusion models
Lecture 15 (Wednesday, March 20): Attention
Lecture 16 (Monday, March 25): Transformers
Lecture 17 (Wednesday, March 27): Markov decision processes
Lecture 18 (Monday, April 1): Reinforcement learning
Lecture 19 (Wednesday, April 3): Reinforcement learning
Lecture 20 (Monday, April 8): Clustering
Lecture 21 (Wednesday, April 10): Latent representations
Patriots' Day Holiday (Monday, April 15) No classes
Lecture 22 (Wednesday, April 17): Decision trees
Lecture 23 (Monday, April 22): Support vector machines
Lecture 24 (Wednesday, April 24): Fairness in ML
Lecture 25 (Monday, April 29): Ethics and responsibility in ML
Last Day of Classes (Wednesday, May 1)