CSE 6363 - Machine Learning (8-week)

Fall 2025

Last updated on Aug 18, 2025

⭓Description

This is the course page for CSE 6363 - Machine Learning (8-week) in Spring 2025. Here you’ll find class notes and other helpful resources. All assignments and announcements will be posted on Canvas.

Instructor Office Hours

Days	Time	Location
MoWeFri	10AM - 11AM	ERB 556A
TuTh	11AM - 12PM	ERB 556A

Teaching Assistant

TBD

⭓Course Materials

Books

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning, Springer, 2023.
Kevin Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2022.

Papers

Class Papers (Zotero)

Resources

⭓Schedule

This schedule is tentative and may change.

Date	Topic	Materials
August 18	Course Introduction, Supervised Learning	Linear Regression Logistic Regression
August 20	Regularization, Probability Theory, and Linear Discriminant Analysis	Regularization Probability Theory Linear Discriminant Analysis
August 25	Naive Bayes Classifier, Support Vector Machine	Naive Bayes Classifier Kernels Support Vector Machine
August 27	Support Vector Machine, Neural Networks	Support Vector Machine Sequential Minimal Optimization Perceptron Neural Networks
September 3	Decision Trees, Boosting and Bagging	Decision Trees Boosting Gradient Boosting
September 8	Gradient Boosting, Deep Learning	Gradient Boosting Convolutional Neural Networks
September 10	CNNs for Object Detection, Python for Deep Learning	CNNs for Object Detection
September 15	Optimization for Deep Learning, Recurrent Neural Networks	Optimization for Deep Learning Automatic Differentiation Recurrent Neural Networks
September 17	Transformers	Transformers Transformers for Computer Vision
September 22	Unsupervised Learning	Clustering Segmentation via Clustering Principal Component Analysis Generative Adversarial Networks
September 24	Reinforcement Learning	Markov Decision Processes Reinforcement Learning Overview Chapters 3-6
September 29	Policy Gradient Methods, Introduction to NLP	Policy Gradient Methods Chapter 13
October 1	Large Language Models
October 6	Project Presentations
October 8	Special Topics