everything.study
SYDE 675

Pattern Recognition

Graduate

Winter 2022

Topics

  • Week 1
  • Introduction. (Reading: Bishop: 1,2; Murphy foundation chapters 2-8)
  • Week 2
  • Linear Models for Regression (Bishop 3; Murphy 4, 11)
  • Week 3
  • Bayesian Decision (Bishop 1.2, 2.3, 3.3; Murphy 3-5)
  • Week 4
  • Linear Model Classifiers (Bishop 4, Murphy 4-5)
  • Week 5
  • Linear Models (Bishop 3,4; Murphy 9-12)
  • Week 6
  • Week 7
  • Non parametric models (KNN) (Murphy 16
  • Week 8
  • Kernels (SVM) (Bishop 6; Murphy 17)
  • Clustering (Murphy 21)
  • Week 9
  • Decision Trees (Murphy 18)
  • Week 10
  • Dimensionality Reduction (PCA, ICA) (Bishop 12; Murphy 20)

Readings

  • Christopher Bishop (2006). Pattern Recognition and Machine Learning, Springer. down- loaded from PRML book download
    required
  • Kevin P. Murphy (2022). Probabilistic Machine Learning. MIT Press. website
    required
Winter 2021

Topics

  • Statistical learning
  • Linear regression and classification
  • Decision theory and Naive Bayes
  • Supervised learning
  • Model selection and regularization
  • Unsupervised learning
  • Neural networks and deep learning

Readings

  • No particular textbook is used; a list of books and resources is supplied during lectures.
    unspecified
  • Weekly self-study Jupyter notebooks in Python.
    unspecified
Sources