Advanced Topics in Machine Learning and Optimization

General information

Degree:	Master of Science in Artificial Intelligence Systems
Period:	September - December

Objectives

The course aims at introducing students to some selected advanced topics on machine learning and optimization. The topics are chosen so as to cover relevant recent research directions and challenges for the machine learning and artificial intelligence community, as well as promising technological advancements.

Prerequisites

A good knowledge of the basics of machine learning (including deep learning) and artificial intelligence (including logic and probabilistic reasoning) is essential. Students are expected to take this course after completing Machine Learning and Fundamentals of Artificial Intelligence from the first year of the AIS degree (or similar teachings).

Content

A preliminary list of topics that will be covered throughout the course is provided below:

Explainable Artificial Intelligence
Statistical Relational Artificial Intelligence
Neuro-Symbolic integration
Graph Neural Networks
Structured-output learning
Interactive Machine Learning
Intepretable-by-design Neural Networks

Course Information

Instructors:	Andrea Passerini Email: Stefano Teso Email: stefano.teso@unitn.it
Office hours:	Arrange by email
Lecture time:	Wednedsay 14:30-16:30 (room a210) Thursday 9:30-11:30 (room a215)
Communications:	Please check the moodle page of the course for news and updates.
Bibliography:	Each topic will be presented with slides accompanied by a list of relevant scientific publications to be used as reference material.
Slides:	Graph Neural Networks [slides] [handouts] [notebook] [notebook (pdf)] Structured Output Prediction [slides] Statistical Relational AI [slides] [handouts] Neuro-Symbolic Integration [slides] [handouts] [lab material (tgz)] Seminar on Learning and Reasoning with Graph Data [slides]
Videos:	Registered lectures (from previous year) made available on Moodle

Exams

Modality:

The student will be asked to realize a machine learning project concerning one or more of the topics seen during the course and write a short report. Suggested approximate length for the report is 5 pages in double-column format plus references. Potential topics will be provided by the teacher. Students are free to propose alternative topics.

The exam will consist of the evaluation of the project (70%) and the discussion of the report and the topics behind it (30%). The student should contact the teacher (or the teaching assistant responsible for the chosen topic) as soon as the report is complete to arrange the discussion. Preliminary versions of the report can also be sent to gather feedback and make adjustments if needed.

Students will be evaluated based solely on their report and interview according to the following criteria:

Is the report clear and well structured? Are technical terms used appropriately? Are mathematical formulas, if any, correct?
Is the work properly positioned against the literature?
Is the model described in sufficient detail? Are all methodological choices motivated?
Does the evaluation address the research question? Are the results appropriately explained? Are negative results, if any, appropriately contextualized? Does the description enable reproducibility?

Please note that the discussion of the report is conducted asynchronously via email and is separate from the official exam date. The exam date is only used for registration purposes after the exam has been completed. Kindly ensure that you submit your report at least one week before the registration date you plan to use.

Projects:

The list of available projects can be found here. Check the current assignment status here to find projects that are still available. Feel free to write the contact person of each project for further information. Once you decided about a project, inform the contact person to have the project assigned to you. Write andrea.passerini@unitn.it for requests about custom projects.