CSE 6363 - Machine Learning

⭓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
MoWe	1PM - 2PM	ERB 556A
TuTh	3PM - 4PM	ERB 556A
Fri	9AM - 10AM	Teams

Teaching Assistant

Mengliang Zhang (mxz3935 (at) mavs (dot) uta (dot) edu)

Office Hours

Please contact Mengliang via email or Teams to schedule an appointment.

Days	Time	Location
Thu	2PM - 6PM	SEIR 428

⭓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
June 2	Course Introduction, Supervised Learning	Linear Regression
June 4	Supervised Learning, Logistic Regression, HW1 Out	Linear Regression Logistic Regression
June 9	Review of Probability Theory, Linear Discriminant Analysis	Probability Theory LDA
June 11	Naive Bayes, Kernels	Naive Bayes Kernels
June 16	Kernels	Kernels
June 18	Support Vector Machine, Neural Networks	Sequential Minimal Optimization Neural Networks
June 23	Backpropagation, Bias-Variance Tradeoff	Neural Networks Bias-Variance Tradeoff
June 25	Decision Trees	Decision Trees
June 30	Boosting	Boosting Gradient Boosting
July 2	Deep Learning	Deep Learning Convolutional Neural Networks
July 7	CANCELLED
July 9	Convolutional Neural Networks, PyTorch Tutorial	Convolutional Neural Networks
July 14	PyTorch Lightning, Optimization for Deep learning	Optimization for Deep learning
July 16	Attention Mechanism, Transformers	Recurrent Neural Networks Transformers
July 21	Markov Decision Processes	Markov Decision Processes
July 23	Reinforcement Learning	Reinforcement Learning Policy Gradient Methods
July 28	Automatic Differentiation, Unsupervised Learning
July 30	Self-Supervised Learning, Large Language Models	Natural Language Processing Pretraining LLMs
August 4	LLM Post-Training, RAG, and Tools	Fine-tuning LLMs Using RAG to Talk to Your Data
August 6	Project Presentations