Course info

Welcome to Machine Learning (2023)


Description: Machine learning is concerned with the development of computer programs that allow computer (or machine) to learn from examples or experiences. Machine learning is of interdisciplinary nature, with roots in computer science, statistics and pattern recognition. In the past decade, this field has witnessed rapid theoretical advances and growing real world applications. Successful applications include machine perception (speech recognition, computer vision), control (robotics), data mining, web search and text classification, time-series prediction, system modelling, bioinformatics, data compression, and many more.

This course will give a comprehensive introduction to machine learning both by presenting technologies proven valuable and by addressing specific problems such as pattern recognition and data mining. This course covers both theory and practices for machine learning, but with an emphasis on the practical side namely how to effectively apply machine learning to a variety of problems. Topics will include:

• Supervised learning (of classification and regression functions)
• K-nearest neighbors, decision trees, naïve Bayes, support vector machines, logistic regression, evolutionary algorithms, Bayesian Networks, hidden Markov model, neural networks, boosting
• Unsupervised learning and clustering
• K-means, hierarchical clustering (agglomerative and divisive), principal component analysis, Expectation Maximization algorithm
• Reinforcement learning

Prerequisites: Basic probability and statistics theory, linear algebra.

Literature (finding one that interests you):

• Machine Learning – A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012
• Introduction to Machine Learning – second edition, Ethem Alpaydin, The MIT Press, 2009
• Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006
• Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.