CSE 4309 - Fundamentals of Machine Learning

⭓Description

This is the course page for CSE 4309 - Fundamentals of Machine Learning in Spring 2026. 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	12PM - 1PM	ERB 556A
TuTh	12PM - 1PM	Teams

Teaching Assistant

Mengliang Zhang (mxz3935@mavs.uta.edu)

Office Hours Tuesday 2:00PM - 6:00PM in ERB 320

⭓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
January 12	Course Introduction
January 14	Supervised Learning	Linear Regression Regularization
January 21	Supervised Learning	Regularization Logistic Regression
January 26	Probability Theory Review	Probability Theory Maximum Likelihood Estimation
January 28	Probabilistic Models	Linear Discriminant Analysis Naive Bayes Classifier
February 2	Kernels and Support Vector Machine	Kernel Methods Support Vector Machine
February 4	Support Vector Machine	Support Vector Machine
February 9	CANCELLED
February 11	Perceptron and Neural Networks	Perceptron Neural Networks
February 16	Decision Trees	Decision Trees
February 18	Bagging, Random Forests, and Adaboost	Adaboost
February 23	Perceptron and Neural Networks	Perceptron Neural Networks
February 25	Intro. to Deep Learning	Deep Learning Convolutional Neural Networks
March 2	PyTorch Lightning Workshop	Pytorch Tutorial