|INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION||INF/01||LEZIONI||72|
The course introduces students to the analysis and design of advanced machine learning and deep learning models for modern pattern recognition problems and discusses how to realize advanced applications exploiting computational intelligence techniques. The course also presents fundamentals of signal and image processing. Particular focus will be given to pattern recognition problems and models dealing with sequential and time-series data. The renewed version of the course (starting on academic year 2021/2022) also introduces fundamentals of reinforcement learning and deep reinforcement learning.
The course is targeted at students who are pursuing specializations in Artificial Intelligence and Machine Learning, but it is of interest for data scientist and information retrieval specialists, roboticists and those with a bioinformatics curricula.
Course examination for students attending the lectures is performed in 2 stages: midterm assignments and an oral presentation. Midterms waive the final project.
Midterm assignments consist in a very short presentation (5 minutes per person) to be given in front of the class, presenting the key/most-interesting aspects of one of the following tasks:
Students might be given some amount of freedom in the choice of assignments, pending a reasonable choice of the topic. The assignments will roughly be scheduled every 1 month.
The oral examination will test knowledge of the course contents (models, algorithms and applications).
Non attending students
Working students and those not attending course lectures will deliver a final project making up for the missing midterms. They will be subject to an oral examination like attending students. Contact the instructor by mail to arrange project topics and examination dates.
The final project concerns preparing a presentation on a topic relevant to the course content or the realization of a software implementing a non-trivial learning model and/or a PR application relevant for the course. The content of the final project will be discussed in front of the instructor and anybody interested during the oral examination. Students are expected to prepare slides for a 15 minutes presentation which should summarize the ideas, models and results in the report. The exposition should demonstrate a solid understanding of the main ideas in the report.
Students completing the course are expected
Students will be given both programming and reading assignments (as part of the midterm or as final projects) to assess their development and research understanding skills.
Course prerequisites include
The official language of the course is English: all materials, references and books are in English. Lecture slides will be made available, together with suggested readings, on the course website.
The topics covered as part of the course include:
The course does not have an official textbook covering all its contents. However, reference books covering parts of the course are listed at the bottom of this section (note that some of them have an electronic version freely available for download).
David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
Ian Goodfellow and Yoshua Bengio and Aaron Courville , Deep Learning, MIT Press
Working students and those not attending course lectures will handle a final project and will also be subject to an oral examination including both an oral presentation of the project as well as an examination on the course program.
Course examination for students attending the lectures is performed in two stages, including midterm assignments and an oral presentation. Midterms provide a waiver for the final project.