DASC 5304 - Machine Learning

⭓DASC 5304 - Machine Learning

This is the course page for DASC 5304 - 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	12PM - 1PM	ERB 556A
TuTh	1PM - 2PM	ERB 556A

Teaching Assistant

Sudharani Bannengala (sxb7066 (at) mavs (dot) uta (dot) edu)

Office Hours

Days	Time	Location
MoWe	11AM - 12PM	ERB 501

⭓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.

Resources

⭓Schedule

This schedule is tentative and may change.

Date	Topic	Materials
January 14	Course Introduction	Linear Regression
January 16	Supervised Learning	Regularization Logistic Regression
January 21	Probability Theory Review	Probability Theory Maximum Likelihood Estimation
January 23	Probabilistic Models	Linear Discriminant Analysis Naive Bayes Classifier
January 28	Kernels and Support Vector Machine	Kernel Methods Support Vector Machine
January 30	Using Python for Machine Learning, Decision Trees	Decision Trees
February 4	Perceptron and Neural Networks	Perceptron Neural Networks
February 6	Ensemble Methods	Random Forest Boosting
February 11	Bagging, Random Forests, and Adaboost	Adaboost
February 13	Gradient Boosting	Gradient Boosting
February 18	Intro. to Deep Learning	Deep Learning Convolutional Neural Networks
February 20	Convolutional Neural Networks	Convolutional Neural Networks Pytorch Tutorial Pytorch Lightning
February 27	Optimization for Deep Learning, Object Detection	Optimization for Deep Learning Object Detection
March 4	Automatic Differentiation	Automatic Differentiation
March 6	Recurrent Neural Networks, Transformers	Recurrent Neural Networks Transformers
March 18	Clustering	Clustering
March 20	Principal Component Analysis	Chapter 12.1
March 25	Segmentation by Clustering, GANs	Segmentation via Clustering Generative Adversarial Networks
March 27	Markov Decision Processes	Markov Decision Processes
April 1	Reinforcement Learning	Reinforcement Learning Overview Chapters 3-6
April 3	Policy Gradient Methods	Policy Gradient Methods
April 8	Natural Language Processing, Large Language Models	Natural Language Processing Pretraining LLMs
April 10	Recitation Day
April 15	LLM Post-Training, RAG	Fine-tuning LLMs Using RAG to Talk to Your Data
April 17	Vision Transformers, Pose Estimation	Transformers for Computer Vision
April 22	Project Presentations
April 24	Project Presentations
April 29	Instance Segmentation	Instance Segmentation