Machine learning (2013-14)
Instructor: Luca Iocchi
Course web page: www.diag.uniroma1.it/~iocchi/Didattica/ml/doku.php
Infostud code: 1022858
The objectives of this course are to present a wide spectrum of Machine Learning methods and algorithms, discuss their properties, convergence criteria and applicability. The course will also present many examples of successful application of Machine Learning algorithms in different application scenarios. The main outcome of the course is the capability of the students of solving learning problems, by a proper formulation of the problem, a proper choice of the algorithm suitable to solve the problem and the execution of experimental analysis to evaluate the results obtained.
Introduction to machine learning. Inductive learning. Decision trees. Evaluation of hypotheses. Bayesian learning. Classification with linear models. Support vector machines. Regression with linear models. Artificial neural networks. Genetic algorithms. Instance Based Learning. Multiple learners and boosting. Bayesian networks. Unsupervised learning and clustering. Hidden Markov Models. Reinforcement learning. Robot learning.
- T.M. Mitchell, "Machine Learning," McGraw-Hill, 1997
- C.M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006