[Machine Learning: A very quick introduction.]
[Introduction to a similar course, written by Amos Storkey at the University of Edinburgh.]
| Serial no. | Topics | Lecture nos. | PRML Chapters | Other References | Slides |
| 1 | Introduction, Revision of Probability Theory and Distributions | 1–8 | 1, 2 | Review of Probability Theory; The Multivariate Gaussian Distribution | Very quick introduction; PRML Chapter 1; 'Beauty' in Scientific Theories |
| 2 | Supervised Learning: Linear Regression Models | 8–10 | 3 | Regression notes | PRML Chapter 3 |
| 3 | Supervised Learning: Classification, Linear Discriminant Analysis | 11–14 | 4 | Generative learning notes | Linear models for classification |
| 4 | Supervised Learning: Kernels, Support Vector Machines | 15–20 | 6, 7 | SVM notes; SVM tutorial; Properties of kernels; Platt's SMO paper; SVM demo | |
| 5 | Supervised Learning: Neural Networks, Deep Learning | 21–27 | 5 | ANN tutorial; Geoff Hinton lecture on deep learning; Convolutional neural nets: tutorial, demos; Sparse autoencoder notes; Baby Human video clip on face recognition; Backpropagation with max pooling notes | Google's unsupervised deep learning |
| 6 | Feature Selection | 28 | |||
| 7 | Unsupervised Learning: Clustering, Mixture Models, Latent Variables, Expectation-Maximisation | 29–32 | 9 | K-means notes; GMM notes; EM notes | Clustering |
| 8 | Unsupervised Learning: Dimensionality Reduction, Factor/Component Analysis | 33–36 | 12 | PCA notes; PCA tutorial; Factor analysis notes | PCA |
| 9 | Naïve Bayes, Probabilistic Graphical Models | 37–41 | 8 | Naïve Bayes notes; Bayesian Networks notes; MRF image denoising example (rough notes) | Graphical Models |
| 10 | Hidden Markov Models | 42 | 13 | HMM notes; Microsoft's photo-real talking head [demo paper] |