Topics in the future are subject to change.
Readings indicate sections in the Bishop textbook. All readings are optional.
| Day | Topic | Reading | Coursework |
| Week 1 | |||
| Mon, Sep 8 | Introduction | ||
| Wed, Sep 10 | Linear regression | ||
| Fri, Sep 12 | Tut: MATLAB | ||
| Week 2 | |||
| Mon, Sep 15 | Basis function regression, KNN regression | 3.1-3.1.5 | A1 out |
| Wed, Sep 17 | Probability theory | 1.2-1.2.5 | |
| Fri, Sep 19 | Tut: Quadratics | C | |
| Week 3 | |||
| Mon, Sep 22 | Estimation; Multidimensional gaussians; Gaussian Class-Conditional model | 2.3-2.3.4, 4.2-4.2.2 | |
| Wed, Sep 24 | Logistic regression | 4.3.2 | |
| Fri, Sep 26 | Tut: Probabilities | ||
| Week 4 | |||
| Mon, Sep 29 | Gradient descent; Classification with neural networks; KNN classification; Naïve Bayes | 5.2.4,2.5.2 | |
| Wed, Oct 1 | Cross validation, Bayesian Regression | 1.3,3.3-3.3.2,3.4 | |
| Fri, Oct 3 | Tut: Bayesian prediction | ||
| Week 5 | |||
| Mon, Oct 6 | Hyperparameter estimation | A1 in; A2 out | |
| Wed, Oct 8 | Model selection and Monte Carlo | 3.4,11 | |
| Fri, Oct 10 | Tut: Importance Sampling and MCMC | 11.1.4,11.2-11.2.2 | |
| Week 6 | |||
| Mon, Oct 13 | Thanksgiving | ||
| Wed, Oct 15 | Tut: A1 | ||
| Fri, Oct 17 | Tut: Last year's midterm/final questions Remarking/help session (2-3pm, BA3201) | ||
| Week 7 | |||
| Mon, Oct 20 | Principal Components Analysis | 12.1.2-12.1.4 | |
| Wed, Oct 22 | Midterm | ||
| Fri, Oct 24 | Tut: PPCA | 12.2 | |
| Week 8 | |||
| Mon, Oct 27 | Lagrange multipliers | E | |
| Wed, Oct 29 | k-means clustering | 9-9.1.1 | |
| Fri, Oct 31 | Tut: LLE, SNE | ||
| Week 9 | |||
| Mon, Nov 3 | Mixtures of Gaussians, Expectation-Maximization | 9.2-9.2.2 | |
| Wed, Nov 5 | Expectation-Maximization and Free Energy | ||
| Fri, Nov 7 | Tut: Markov Models | 13-13.1 | |
| Week 10 | |||
| Mon, Nov 10 | Hidden Markov Models, Viterbi algorithm | 13.2, 13.2.5 | A2 in; A3 out |
| Wed, Nov 12 | Forward-Backward algorithm, EM | 13.2.1-13.2.2 | |
| Fri, Nov 14 | No tutorial | ||
| Week 11 | |||
| Mon, Nov 17 | Karush-Kuhn-Tucker; Applications of HMMs | ||
| Wed, Nov 19 | Support vector machines | 7 | |
| Fri, Nov 21 | Tut: Scaling and the Forward-Backward algorithm | 13.2.4 | |
| Week 12 | |||
| Mon, Nov 24 | Ensemble learning, Boosting, AdaBoost | 14-14.3 | |
| Wed, Nov 26 | AdaBoost | 14.3.1 | |
| Fri, Nov 28 | Tut: Face detection; A3 help | ||
| Week 13 | |||
| Mon, Dec 1 | Guest lecture: ML and Computational Biology | ||
| Wed, Dec 3 | Wrap-up; final exam info | ||
| Fri, Dec 5 | Tut: Restricted Boltzmann Machines and Deep Belief Networks; A3 help | A3 in |