General information
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Degree:
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Master in Computer Science
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Faculty:
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Scienze MM.FF.NN.
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Period:
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September 2010 - December 2010
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Objectives
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Provide knowledge of both theoretical and
practical aspects of machine learning, of the main
techniques of supervised and unsupervised learning
and probabilistic reasoning. Show applications of
machine learning techniques to real world
problems.
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Prerequisites
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Linear algebra, probability theory (briefly
revised during the course). Boolean algebra,
knowledge of a programming language.
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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. Neural
networks: perceptron, multilayer neural
networks. Clustering: k-means, hierarchical
clustering. Kernel Machines: kernels, reproducing
kernel Hilbert spaces, representer theorem,
support vector machines for classification,
regression and ranking, kernel construction,
kernels for structured data. Statistical Learning
Theory: PAC learning, consistency, VC dimension,
generalization and models comparison. Applications
to text categorization and bioinformatics.
Course Information
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Instructor:
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Andrea Passerini
Email: 
joint course with Alessandro Moschitti. See his
homepage for his own material
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Office hours:
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Wednesday 10:30-12:30
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Lecture time and place:
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Tuesday 14-16
Wednesday 8:30-10:30
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Bibliography:
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R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification (2nd edition),
Wiley-Interscience, 2001.
C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006
T. Mitchell, Machine Learning, McGraw Hill, 1997.
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Material:
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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]
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]
Expectation Maximization [slides] [handouts]
Hypothesis Testing [slides] [handouts]
Clustering [slides] [handouts]
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Exams
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Modality:
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Project [description] and oral examination
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Projects:
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(1) Hierarchical gene clustering [desc] [data] (to: Andrea Zito)
(2) Patient classification by gene expression [desc] [data] (to: Lucas Mariano)
(3) Bayesian Network of pathologies [desc] [data] (to: Danilo Tomasoni e Rino Napo)
(4) Protein subcellular localization [desc] [data] (to: Bhavani Vaidya)
(5) Disulphide bonding state prediction [desc] [data] (to: Mauro Fruet and Mattia Gastaldello)
(6) Comparing learning algorithms [desc] [data] (to: Fabrizia Toss and Antonio Quartulli)
... additional projects will follow ...
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