CSE 6363 - Machine Learning

Fall 2025

Last updated on Aug 18, 2025

⭓Description

This is the course page for CSE 6363 - Machine Learning 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

Roman Strukov (rxs6055@mavs.uta.edu)

⭓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
August 20	Supervised Learning	Linear Regression Regularization
August 25	Classification	Logistic Regression
August 27	Multi-class Classification	Logistic Regression Regularization
August 29	Regularization	Regularization
September 3	Probability Theory Review	Probability Theory Maximum Likelihood Estimation
September 5	Probabilistic Models	Linear Discriminant Analysis Naive Bayes Classifier
September 8	Kernels	Kernels
September 10	Support Vector Machine	Support Vector Machine
September 12	Support Vector Machine	Support Vector Machine
September 15	Neural Networks	Neural Networks
September 17	Convolutions	Convolutions
September 19	Convolutional Neural Networks	Convolutional Neural Networks
September 22	Convolutional Neural Networks	Convolutional Neural Networks
September 24	Introduction to PyTorch
September 26	Decision Trees	Decision Trees
September 29	Decision Trees	Decision Trees
October 1	Adaboost	Boosting
October 3	Gradient Boosting	Gradient Boosting
October 6	Recurrent Neural Networks	Recurrent Neural Networks
October 8	Transformers	Transformers
October 10	Transformers	Transformers
October 13	Unsupervised Learning - Clustering
October 15	Principal Component Analysis	Principal Component Analysis
October 17	Principal Component Analysis	Principal Component Analysis
October 20	Markov Decision Processes	Markov Decision Processes
October 22	Reinforcement Learning	Reinforcement Learning
October 24	Policy Gradient Methods
October 27	Class Cancelled
October 29	Deep Reinforcement Learning
October 31	Object Detection	Object Detection
November 3	Instance Segmentation	Instance Segmentation
November 5	Automatic Differentiation	Automatic Differentiation
November 7	Vision Transformers	Vision Transformers
November 10	Natural Language Processing, Large Language Models	Natural Language Processing Pretraining LLMs
November 12	LLM Post-Training, RAG	Fine-tuning LLMs Using RAG to Talk to Your Data
November 14	Transformer-based Detection and Segmentation	Vision Transformers
November 17	Project Presentations
November 19	Project Presentations
November 21	Project Presentations
November 24	Project Presentations