[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: Nature of Intelligence and Learning; Revision of Probability Theory, Distributions, Bayesian Inference | 1–8 | 1, 2 | Review of Probability Theory; The Multivariate Gaussian Distribution; Mathematics for Machine Learning | |
| 2 | Basics of Learning: Tasks, Models, Features | 8–10 | Flach Chapter 1 | Very quick introduction | |
| 3 | Supervised Learning: Linear Regression Models | 11–18 | 1, 3 | Regression notes | PRML Chapter 1; 'Beauty' in Scientific Theories; PRML Chapter 3 |
| 4 | Supervised Learning: Classification, Linear Discriminant Analysis, the Perceptron | 19–21 | 1, 4 | Linear models for classification | |
| 5 | Supervised Learning: Support Vector Machines, Kernels | 22–23; 25–28 | 6, 7 | SVM notes; SVM tutorial; Properties of kernels; Platt's SMO paper; SVM demo | |
| 6 | Supervised Learning: Probabilistic models—Logistic Regression, Naive Bayes | 24; 28 | 4, 8 | Regression notes; Generative learning notes | Linear models for classification |
| 7 | Supervised Learning: Neural Networks, Deep Learning | 29–34 | 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 |
| 8 | Unsupervised Learning: Clustering, Mixture Models, Latent Variables, Expectation-Maximisation | 34–37 | 9 | K-means notes; GMM notes; EM notes | Clustering |
| 9 | Unsupervised Learning: Dimensionality Reduction, Factor/Component Analysis | 38–39 | 12 | PCA notes; PCA tutorial; Factor analysis notes | PCA |