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Lecture Notes and Slides
Logistic Regression: From Binary to Multi-Class [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]
Back-Propagation: 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.
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Past
Spring 2024: CSCE 636: Deep Learning
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
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