Machine Learning (CS)

• DegreeMaster in Computer Science
• PeriodSeptember – December
• InstructorAndrea Passerini
• Teaching assistants Steve Azzolin
  Samuele Bortolotti
• Office hoursArrange by email.
• Lecture time Monday 8:30–10:30 (room a106)
  Wednesday 11:30–13:30 (room a104)
• 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
  I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, The MIT Press, 2016 (online version)
  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.

Provide knowledge of both theoretical and practical aspects of machine learning. Present the main techniques of machine learning and probabilistic reasoning.

Linear algebra, probability theory (briefly revised during the course). 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.

Introduction to machine learning: designing a machine learning system, learning settings and tasks, decision trees, k-nearest-neighbour estimation. Mathematical foundations: linear algebra, probability theory, statistical tests. Maximum likelihood and Bayesian parameter estimation. Probabilistic graphical models: formalization, parameters and structure learning. Discriminative learning: linear discriminant functions, support vector machines, kernel machines. Neural networks: representation learning, deep architectures. Ensemble methods. Unsupervised and reinforcement learning.

• Introductionslides handouts
• Decision Treesslides handouts
• K-nearest neighboursslides handouts
• Linear algebraslides handouts
• Probability theoryslides handouts
• Evaluationslides handouts
• 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
• Artificial Neural Networksslideshandouts
• Artificial Neural Networks labrepository
• Ensemble Methodsslides handouts
• Unsupervised learningslides handouts
• Unsupervised learning labrepository
• Reinforcement learningslides handouts
• Reinforcement learning labrepository