Lecture Notes and Slides
Logistic Regression: From Binary to MultiClass [Notes] [Slides]
A Unified View of Loss Functions in Supervised Learning [Notes] [Slides]
A Neural Network View of Kernel Methods [Notes] [Slides]
Principal Component Analysis and Autoencoders [Notes] [Slides]
BackPropagation: From Fully Connected to Convolutional Layers [Notes] [Slides]
A Mathematical View of Attention Models in Deep Learning [Notes] [Slides]
Unsupervised Learning and Expectation Maximization [Slides]
Attention, Transformers, and Large Language Models [Slides]
Graph Neural Networks [Slides]
These materials can be used freely for teaching and research purposes,
as long as they are cited properly.
Bibtex is here.
Current
FAQ
Please read before sending inquiries.
Past
Fall 2023: CSCE 421: Machine Learning
Fall 2022: CSCE 636: Deep Learning
Fall 2021: CSCE 636: Deep Learning
Fall 2020: CSCE 636: Deep Learning
Fall 2019: CSCE 636: Deep Learning
Spring 2019: CSCE 489: Machine Learning
Fall 2018: CSCE 636: Deep Learning
Spring 2018: CPTS 437: Introduction to Machine Learning
Spring 2017: CPTS 483: Introduction to Machine Learning
Fall 2016: CPTS 580: Deep Learning
Spring 2016: CPTS 483: Introduction to Machine Learning
Spring 2015: CS795/895: Machine Learning for Neuroimaging
Fall 2014: CS722/822 Machine Learning
Spring 2014: CS 361 Advanced Data Structures and Algorithms
Spring 2014: CS 600 Algorithms and Data Structures
Fall 2013: CS722/822 Machine Learning
Spring 2013: CS795/895 Advanced Biological Data Mining
Fall 2012: CS795/895 Machine Learning
Spring 2012: CS600 Algorithms and Data Structures
Spring 2012: CS795/895 Data Mining and Bioinformatics
Fall 2011: CS795/895 Machine Learning
Spring 2011: CS600 Algorithms and Data Structures
Fall 2010: CS480/580 Introduction to Artificial Intelligence
