Course content

• 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.

References

• 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)