CS 539: Machine Learning :: Summer 2026

Primary Textbook is

• (Flach) Machine Learning: The Art and Science of Algorithms That Make Sense of Data by Peter Flach, Cambridge University Press.

Additional course readings will be drawn from the following textbooks:

• (LfD) Learning From Data by Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.T. Lin., AML Book
• (Mitchell) Machine Learning by Tom Mitchell, McGraw Hill
• Machine Learning Lecture Notes by Andrew Ng
• (Sutton & Barto) Reinforcement Learning: An Introduction by Sutton and Barto, MIT Press
• (DLP) Deep Learning with Python by François Chollet, Manning Publications
• Selected Research Papers

Tentative Schedule (subject to change)

Week #	Date	Topic	Readings	Homework	Quiz	Project
1	5/26	Course overview & Machine Learning, Framing a Learning Problem; Supervised Learning & Linear Regression; Gradient Descent	Flach: Prologue & Ch. 1, The Discipline of Machine Learning; LfD: 1.1; Flach: 7.1; LfD: 3.2.1
2	6/2	Linear Regression; Regularization; Overfitting, k-fold cross-validation	Basis functions Optional linear algebra review	HW1 out		Form a team by June 9
3	6/9	Logistic Regression; Evaluation; Optimizers; Why Machine Learning Works: VC Dimension & Generalization Bounds	LfD: 2, 3.2.2, 3.3, Learning Theory Notes	HW2 out	Quiz1
4	6/16	Neural Network; Deep Learning;	Deep Learning Backpropagation
5	6/23	PyTorch Basics and Example; Deep Learning; Ensemble Methods;	DLP: 5.1; Convolutional Neural Networks DLP: 6.2; Flach: 11	HW3 out	Quiz2
6	6/30	Unsupervised Learning:  K-Means; GMMs; Markov Decision Process;	k-means clustering Flach 8.4 Mixture of Gaussians Sutton & Barto: Ch. 3, Ch. 4, RL notes			Proposal due by June 30
7	7/7	Markov Decision Process; Reinforcement Learning;		HW4 out
8	7/14	Support Vector Machines; Text Classification; Word Vectors;	Flach: 7.3, 7.5, Bennett Distributed Representations of Words and Phrases and their Compositionality		Quiz3
9	7/21	Transfer Learning; Distillation	Attention Is All You Need BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
10	7/28	Parameter-Efficient Finetuning; Project Presentation (live session)			Quiz4 (7/28~7/29)	Website and Slides due by July 28 Peer & Self Evaluation Form by July 29