Machine Learning (AIS): Module I

• DegreeMaster of Science in Artificial Intelligence Systems
• PeriodSeptember – December
• InstructorAndrea Passerini
• Teaching assistants Steve Azzolin
  Samuele Bortolotti
• Office hoursArrange by email.
• Lecture time Monday 11:30–13:30 (room a205)
  Tuesday 10:30–12:30 (room a105)
  Wednesday 9:30–11:30 (room a102)
  Thursday 9:30–11:30 (room a103)
• CommunicationsPlease check the Moodle page of the course for news and updates.
• Bibliography R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification (2nd edition), Wiley-Interscience, 2001
  D. Koller and N. Friedman, Probabilistic Graphical Models, The MIT Press, 2009
  J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004
  S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (4th Global ed.), Pearson, 2021
  K. Murphy, Probabilistic Machine Learning: An Introduction, The MIT Press, 2021 (online version)
• VideosRegistered lectures (from previous year) made available on Moodle.
• ExamOral examination.

Linear algebra, probability theory, boolean algebra, knowledge of a programming language. For a good introduction to linear algebra see: Gilbert Strang, Introduction to Linear Algebra, Wellesley-Cambridge Press, 2016.

The second part of Module I (taught by Andrea Passerini) covers: linear discriminant analysis (perceptron, least-square regression, SVM); kernel machines (kernel methods, kernels on structures); learning Bayesian networks (parameter learning, structure learning); reinforcement learning; unsupervised learning.

• Parameter estimationslides handouts
• Bayesian Networksslides handouts
• Learning BNslides handouts
• Naive Bayesslides handouts
• Bayesian Network labslides data software
• Linear discriminant functionsslides handouts
• Support Vector Machinesslides handouts
• Kernel Machinesslides handouts
• Scikit-learn labslides repository
• Ensemble Methodsslides handouts
• Unsupervised learningslides handouts
• Unsupervised learning labrepository
• Reinforcement learningslides handouts
• Reinforcement learning labrepository

• Linear algebraslides handouts
• Probability theoryslides handouts