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COMP 5011

Machine Learning and Neural Networks

0.5 credits · Graduate

Fall 2023

Topics

  • Learning paradigms
  • regression and gradient descent
  • PCA
  • nearest neighbours
  • Bayesian classifiers
  • trees and ensembles
  • support vector machines
  • clustering
  • neural networks, RNNs/LSTMs and CNNs
  • explainable AI.

Readings

  • Suggested: Christopher M. Bishop, Pattern Recognition and Machine Learning; Kevin Murphy, Machine Learning: A Probabilistic Perspective; Tom Mitchell, Machine Learning; Hastie, Tibshirani and Friedman, The Elements of Statistical Learning; David MacKay, Information Theory, Inference, and Learning Algorithms.
Sources