Machine Learning
Sapienza University of Rome Italy
Master in Artificial Intelligence and Robotics
Master in Engineering in Computer Science
Master in Control Engineering
Master in Engineering in Computer Science
Master in Control Engineering
A.Y. 2020/21
Prof. Luca Iocchi, Fabio Patrizi, Roberto Capobianco
Tutor Lorenzo Brigato
Information
Description of the course
The course gives 9 CFU in the Master degrees in Artificial Intelligence and Robotic (MARR) and 6 CFU for Engineering in Computer Science (MINR), Control Engineering (MCER) and others.
Objectives
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 examples of successful application of Machine Learning algorithms in different application scenarios.
Syllabus
Content for 6 CFU
Classification
Basic concepts and evaluation
Decision Trees
Bayes Learning
Linear Models
Support Vector Machines
Kernels
Multiple classifiers
Regression
Linear and logistic regression
Instance based (K-NN)
Perceptron
Neural networks
Deep neural networks (CNN)
Unsupervised learning
Clustering (k-Means)
Latent variables (EM)
Reinforcement learning
MDP Q-learning
Additional content for 3 CFU (MARR and Artificial Intelligence 2)
Probabilistic Reasoning
Reinforcement Learning (advanced topics)
Deep Reinforcement Learning
Teaching Material
Provided in the Google Classroom system
IMPORTANT Registration with classroom system will reveal to all the subscribers (i.e., teachers and other students) your name and photo used in your Google account. Please make sure the account you use to register to classroom contains relevant, appropriate and privacy-preserving information (e.g., just your first and last name or matricola code), as they will be used also for the evaluation.
Classroom registration code
Other Suggested textbooks
Machine Learning, Tom Mitchell.
Pattern Recognition and Machine Learning, Chris Bishop
Machine Learning: a Probabilistic Perspective, Kevin Murphy
Introduction to Machine Learning, Ethem Alpaydin
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville
Teachers
Prof. Luca Iocchi (Home page)
Prof. Fabio Patrizi (Home page)
Prof. Roberto Capobianco (Home page)
Tutor Lorenzo Brigato (Home page)
Dipartimento di Ingegneria informatica automatica e gestionale “Antonio Ruberti”
Università di Roma “La Sapienza”
Via Ariosto 25, Roma 00185, Italy.
E-mail: {lastname}@diag.uniroma1.it
In e-mail messages, please use the prefix "[ML]" to the subject.
Schedule
IMPORTANT Information about lecture modalities and instructions related of COVID-19 situation are available here
Lecture period 23/9/2020 - 18/12/2020
Group 1 - MARR - 6 CFU
Prof. Luca Iocchi
Monday 10.00-13.00 - Room B2 Via Ariosto 25
Thursday 14.00-16.00 - Room B2 Via Ariosto 25
Group 2 - MINR + MCER + others (including Erasmus) - 6 CFU
Prof. Fabio Patrizi
Monday 10.00-13.00 - Room 21 Via Eudossiana 18
Thursday 14.00-16.00 - Room 24 Via Eudossiana 18
Group 3 - MARR + Artificial Intelligence 2 - 3 CFU
Prof. Roberto Capobianco
Monday 08.00-10.00 - Room B2 Via Ariosto
Exceptionally Tuesdays 16-18
Office hours
During lecture period
Prof. Iocchi - Thursday 16.00-17.30 - Online Zoom meeting
https://uniroma1.zoom.us/j/85043908588?pwd=TVV2Q3pFL2N5VjVmdEt4bitHM0Qydz09
Meeting ID: 850 4390 8588 Passcode: 789578
Note: office hours will be carried out as individual meetings, students will wait in the waiting room until their turn.
Prof. Patrizi - See here for details.
Prof. Capobianco - See here for details. - Online only
Tutor Brigato - Friday 17:00-18:30 - Online Google Meet meeting: link (please send an email no later than one day before)
For office hours after the end of the course ask for confirmation by e-mail.
News
Exams
Exams
The main validation method will be a written test during the regular exam sessions. For 9 CFU, the exam contains an additional part on Reinforcement Learning.
During the lectures, teachers will assign some homeworks.
The homeworks are not mandatory and if delivered within the specified deadlines will be used as bonus marks for the final grade.
To attend an exam, it is mandatory to:
register to the exam with the system INFOSTUD
bring an ID card
Homeworks
Two homeworks will be assigned during the course. Each homework consists in the solution of a learning problem.
Each student has to submit an individual report (PDF file) and the source code (ZIP file without data) through the Google Classroom assignment (e-mail submissions will not be accepted). Please notice that the report (PDF file) must not be included in the ZIP file.
Only individual reports will be accepted. In case of collaborative work, each student has to submit an individual report in which s/he clearly specifies which parts have been developed jointly and which is his/her individual contribution. Make sure your report contains your name and matricola code.
The technical report should contain: a precise description of the learning problem considered, a description of the algorithm used (including parameters and other implementation choices), and a description of the results obtained (with details about the evaluation procedure). The report should also compare at least two different scenarios (e.g., different algorithms, different parameters of an algorithm, different data sets, etc.).
The source code should contain the code actually developed by the student (excluding external libraries and data sets). In case of use of other software (e.g., examples found on Internet), clearly specify which portion have been modified by the student. In case of joint work with other students, clearly specify the portion of code developed by each single student.
Each homework delivered within the deadline and describing the work done in a good quality report will grant up to 2 extra points in the final grade of the exam. A maximum of 4 extra points can be gained with homeworks.
Exam dates
Exam dates are available in
https://corsidilaurea.uniroma1.it/en/corso/2020/30431/programmazione
NOTE: March/April and October/November sessions are reserved to part-time and out-of-course students
For online exams, please read carefully the exam instructions available here
Notes for students who have to pass AIML course (6 CFU).
The ML part for AIML (3 CFU) will be taken as part of the written test of this exam. Students have to attend the written exam in the specified dates and answer three questions out of six at their choice.