CSC2515 Fall 2004 - Lectures

Tentative Lecture Schedule

  • Sept 14 -- Machine Learning:
    Introduction to Machine Learning, Generalization and Capacity (notes [ps] [pdf])

  • Sept 21 --Classification 1:
    KNN, linear discriminants, decision trees (notes [ps] [pdf])

  • Sept 28 -- Classification 2:
    probabilistic classifiers: class-conditional Gaussians, naive Bayes, logistic regression, neural nets for classification (notes [ps] [pdf])

  • Oct 5: Assignment 1 (Classification) posted

  • Oct 5 -- Regression 1:
    constant model, linear models, generalized additive models (e.g. RBFs), locally weighted regression, multilayer perceptrons/neural networks (notes [ps] [pdf])

  • Oct 12 -- Objective Functions and Optimization:
    error surfaces, weight space, gradient descent, stochastic gradient, conjugate gradients, second order methods, convexity, enforcing constraints (notes [ps] [pdf])

  • Oct19: Assignment 1 due at the start of class

  • Oct 19 -- 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 models (notes [ps] [pdf])

  • Oct26: Assignment 2 (Regression) posted

  • Oct 26 -- Unsupervised Learning 1: Trees & Clustering
    K-means, heirarchical clustering (alglomerative and divisive), maximum likelihood trees, optimal tree structure (notes [ps] [pdf])

  • Nov 2 -- 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 9: Assignment 2 due

  • November 9 -- Unsupervised Learning 3:
    Continuous latent variable models, Factor Analysis, (Probabilistic) PCA, Mixtures of Factor Analyzers, Independent Components Analysis (notes [ps] [pdf])

  • Nov 16: Assignment 3 posted

  • Nov 16 -- Time Series Models
    autoregressive/Markov models, hidden Markov models (notes [ps] [pdf])

  • Nov 23 -- 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 30: Assignment 3 due at the start of class

  • Nov 30 -- Meta-Learning Methods:
    stacking, bagging, boosting (notes [ps] [pdf])

  • Dec 7 -- Kernel methods:
    the kernel trick, support vector machines, kernel perceptrons, sparsity, capacity control, dual problems (notes [ps] [pdf])

  • December 20 -- projects due by email before 5pm
    Send attachments or valid URL pointing to your report.

  • December 20 -- all readings must be completed by 5pm.
    email csc2515readings@cs

  • 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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