Machine Learning
General information
Degree: | Master in Computer Science |
Period: | September - December |
Objectives
Provide knowledge of both theoretical and practical aspects of machine learning. Present the main techniques of machine learning and probabilistic reasoning. |
Prerequisites
Linear algebra, probability theory (briefly revised during the course). Boolean algebra, knowledge of a programming language. For a good introduction to linear algebra see: Gilber Strang, Introduction to Linear Algebra, Wellesley-Cambridge Press, 2016. |
Content
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. Bayesian decision theory, maximum likelihood and Bayesian parameter estimation. Probabilistic graphical models, inference, parameters and structure learning. Discriminative learning: linear discriminant functions, support vector machines, kernels for vectorial and structured data. Neural networks: representation learning, deep architectures. |
Course Information
Instructor: |
Andrea Passerini Email: |
Teaching assistant: |
Luca Erculiani Email: luca.erculiani@unitn.it |
Office hours: |
Thursday 14:30-15:30 (send email before) |
Lecture time and place: |
Wednesday 11:30-13:30 (room a106) Thursday 11:30-13:30 (room a205) |
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 available here). |
Material: |
Slides and handouts (pdf format) Introduction [slides] [handouts] Decision Trees [slides] [handouts] K-nearest neighbours [slides] [handouts] Linear algebra [slides] [handouts] Probability theory [slides] [handouts] Evaluation [slides] [handouts] Bayesian decision theory [slides] [handouts] Parameter estimation [slides] [handouts] Bayesian Networks [slides] [handouts] Inference in BN [slides] [handouts] Learning BN [slides] [handouts] Naive Bayes [slides] [handouts] Linear discriminant functions [slides] [handouts] Support Vector Machines [slides] [handouts] Non-linear Support Vector Machines [slides] [handouts] Kernel Machines [slides] [handouts] Kernels [slides] [handouts] Deep Networks [slides] [handouts] |