Machine Learning (2024)

Welcome to Machine Learning (2024)

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 decades, 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, prediction, clustering, generative modeling and anomaly detection. 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 methods: logistic regression, support vector machines, neural networks, K-nearest neighbors, decision trees, boosting
  • Unsupervised learning and clustering methods: K-means, Gaussian mixture models, Expectation Maximization algorithm, principal component analysis
  • Deep learning methods: deep neural networks, long short-term memory recurrent neural networks, convolutional neural networks, generative adversarial networks.
  • Probabilistic graphical models
  • Reinforcement learning

Prerequisites: Basic probability and statistics theory, linear algebra and basic programming skills.

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
• Machine Learning: A Bayesian and Optimization Perspective, Sergios Theodoridis, Academic Press, 2020.

• 18 March: Fibigerstræde 10 room 09
  19 March: Fredrik Bajers Vej 7A room 4-106
  20 March: