- Introduction to machine learning: What is learning, learning objectives, data needed.
- Supervised Learning.
- Bayesian inference and learning: Inference, naïve Bayes.
- Measures of success and loss functions.
- Generalization and model complexity, bias-variance tradeoff. Training, validation, and testing.
- Introduction to convex optimization. Convergence and training time. Objective functions for classification, regression, and ranking.
- Linear regression, Perceptron and logistic regression.
- MLP and backpropagation.
- Deep learning, CNN and RNN.
- SVM, support vector regression, increase in dimensionality through simple kernels. Decision trees.
- Role of randomization and model combination, bagging and boosting.
- Unsupervised Learning. Clustering criteria, K-means, DB-scan.
- Kernel Density estimation. EM-algorithm for mixture of Gaussians.
- Dimensionality reduction using PCA and Kernel-PCA.
- Other Topics: Overview of Reinforcement Learning. Bias and Ethics in ML.
- Optional topics: Introduction to one or more of the following topics: Active/Transfer Learning, Bootstrapping, Semi-supervised learning, Generative and probabilistic graphical models, Online/incremental learning.

- Pattern Recognition and MachineLearning, by Christopher Bishop,Springer 2011
- The Elements of Statistical Learning:Data Mining, Inference, and Prediction,Second Edition, by Trevor Hastie andRobert Tibshirani (Springer Series inStatistics) 2016
- Supplementary material available online,e.g. Dive into Deep Learning by AstonZhang, Zack C. Lipton, Mu Li andAlexander Smola, 2020 (https://d2l.ai)

Pre-requisite | : | N/A |

Total credits | : | 6 credits - Lecture |

Type | : | Core Course |

Duration | : | Full Semester |

Name(s) of other Academic units to whom the course may be relevant | : | N/A |