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]

These materials can be used freely for teaching and research purposes, as long as they are cited properly.
Bibtex is here.

Fall 2020

FAQ

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Past

  • 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