Master in Artificial Intelligence and Robotics


Luca Iocchi

A.A. 2013/2014


This course in A.Y. 2014/2015 will be replaced by
Learning in Autonomous Systems (LAS)

See this page for additional information about this change.

The topics addressed in Machine Learning have been distributed as follows:

  • Classification, regression, unsupervised learning in AIML
    (starting from November 3rd, 2014)
  • Dynamic models, probabilistic models and reinforcement learning in LAS
    (second semester).

Students can attend the corresponding lectures of AIML and LAS to prepare the ML exam.


Prof. Luca Iocchi (Home page)

Dipartimento di Ingegneria informatica automatica e gestionale
“Antonio Ruberti”
Università di Roma “La Sapienza”
Via Ariosto 25, Roma 00185, Italy.
Room B115

Office hours:
(please send an e-mail for an appointment)

Moodle course
Registration is mandatory. Use your real name and ID (matricola).
Some assignments will be evaluated for the final grade.
All the information and submitted material will be kept confidential
and used only for the purposes of this course.


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.


  1. Introduction to machine learning
  2. Inductive learning
  3. Decision trees
  4. Evaluation of hypotheses
  5. Bayesian learning
  6. Classification with linear models
  7. Support vector machines
  8. Regression with linear models
  9. Artificial neural networks
  10. Genetic algorithms
  11. Instance Based Learning
  12. Multiple learners and boosting
  13. Bayesian networks
  14. Unsupervised learning and clustering
  15. Hidden Markov Models
  16. Reinforcement learning
  17. Robot learning

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