CSC2515 Fall 2003 - Lectures

Tentative Lecture Schedule

    Lecture 1: Introduction to Machine Learning, Generalization and Capacity (notes [ps] [pdf])

  • Sept 16 --Classification 1:
    KNN, linear discriminants, decision trees (notes [ps] [pdf])

  • Sept 23 -- Classification 2:
    naive Bayes, logistic regression, neural nets for classification (notes [ps] [pdf])

  • Sept 30: Assignment 1 (Classification) posted

  • Sept 30 -- Regression 1:
    constant model, linear models, generalized additive models (e.g. RBFs), locally weighted regression, multilayer perceptrons/neural networks (notes [ps] [pdf])

  • Oct 7 -- Objective Functions and Optimization:
    error surfaces, weight space, gradient descent, stochastic gradient, conjugate gradients, second order methods, convexity, enforcing constraints (notes [ps] [pdf])

  • Oct14: Assignment 1 due at the start of class

  • Oct 14 -- Regression 2 and Supervised Mixtures:
    credit assignment problem, neural networks, radial basis networks, kolmogorov's theorem, backprop algorithm for efficiently computing gradients, mixtures of experts, piecewise linear models (notes [ps] [pdf])

  • Oct21: Assignment 2 (Regression) posted

  • Oct 21 -- Unsupervised Learning 1: Trees & Clustering
    K-means, heirarchical clustering, soft competitive learning (Mixtures of Gaussians), maximum likelihood trees, optimal tree structure (notes [ps] [pdf])

  • Oct28 -- Unsupervised Learning 2: Mixture models and the EM Algorithm:
    missing data, hidden variables, Jensen's inequality, lower bound on marginal likelihood, free energy interpretation, inference, (notes [ps] [pdf])

  • Nov 4: Assignment 2 due

  • November 4 -- Unsupervised Learning 3:
    Continuous latent variable models, Factor Analysis, (Probabilistic) PCA, Mixtures of Factor Analyzers, Independent Components Analysis (notes [ps] [pdf])

  • Nov 11: Assignment 3 posted

  • Nov 11 -- Time Series Models
    autoregressive/Markov models, hidden Markov models (notes [ps] [pdf])

  • Nov 18 -- Capacity Control:
    generalization and overfitting, No free lunch theorems, high dimensional issues.
    capacity control methods: weight decay, early stopping, cross validation, model averaging, intro to Bayesianism (notes [ps] [pdf])

  • Nov 25: Assignment 3 due at the start of class

  • Nov 25 -- Meta-Learning Methods:
    stacking, bagging, boosting (notes [ps] [pdf])

  • Dec 2 -- Kernel methods:
    the kernel trick, support vector machines, kernel perceptrons, sparsity, capacity control, dual problems (notes [ps] [pdf])

  • December 19 -- projects due

  • Extra topics we may or may not have time for
    • other kernel machines: gaussian processes
    • linear dynamical systems, Kalman filtering
    • Approximate inference and learning:
      sampling, variational approximations, loopy belief propagation
    • Spectral Methods:
      Isomap,LLE, spectral clustering,
    • MaxEnt models:
      maximum entropy/energy-based models, iterative scaling, products of experts, dependency nets,
    • Matrix Factorizations:
      aspect models/LDA/plaids, non-negative matrix factorization
    • Automatic Structure Learning:
      sparsity priors, empirical Bayes, automatic relevance determination (MLII), structural EM

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