CS540 Introduction to Artificial Intelligence


CS540, Fall 2026

Department of Computer Sciences

University of Wisconsin-Madison


# Schedule (Subject to Change)


Slides will be updated the week they are covered.

Week Date Topic Lecture Notes Assignments
1 Sep 2 Introduction, Perceptron, Logistic Regression - -
2 Sep 9 Neural Network, Gradient Descent, Genetic Algorithm - -
3 Sep 16 Support Vector Machines, Decision Trees, K-Nearest Neighbors - -
4 Sep 23 Computer Vision, Convolutional Neural Network - -
5 Sep 30 Graph Neural Network, Generative Adversarial Network, Diffusion Models - -
6 Oct 7 Natural Language Processing, Naive Bayes - -
7 Oct 14 Recurrent Neural Network, Large Language Models - -
8 Oct 21 Principal Component Analysis, T-Distributed Stochastic Neighbor Embedding - -
9 Oct 28 Hierarchical Clustering, K Means Clustering, Spectral Clustering - -
10 Nov 4 Iterative Deepening Search, Dijkstra's Algorithm - -
11 Nov 11 Greedy Search, A* Search, Local Search - -
12 Nov 18 Game Theory, Minimax and Alpha-Beta Pruning - -
13 Nov 25 Markov Decision Process, Reinforcement Learning - -
14 Dec 2 Deep Reinforcement Learning, Multi-Agent System - -
15 Dec 9 Ethics and Trust in AI, Adversarial Machine Learning - -


📗 (RN) Russell and Norvig: Chapters from the optional textbook: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig Link.
📗 Math and Statistics Review:
➩ Professor Jerry Zhu's "math crib sheet" (all the math you need to know for the course): Link.
➩ Calculus (for references only): Link.
➩ Linear Algebra (for references only): Link.
➩ Probability and Statistics (for references only): Link.
📗 Python Crash Course: PPTX

📗 Past exams by Professor Jerry Zhu: Link





Last Updated: April 18, 2026 at 1:45 AM