Topic outline

  • Intelligent Systems for Pattern Recognition A.A. 2018-19 (ISPR)

    Code: 651AA, Credits (ECTS): 6, Semester: 2, Official Language: English

    Instructor: Davide Bacciu

    Contact: email - phone 050 2212749

    Office: Room 367, Dipartimento di Informatica, Largo B. Pontecorvo 3, Pisa

    Office Hours: Thursday, 16-18 (email to arrange meeting)

    • This topic


    • Course Information

      Weekly Schedule

      The course is held on the second term. The preliminary schedule for A.A. 2019/20 is provided in table below.

      The first lecture of the course will be on Thursday 20/02/2020.

      Time    Room  
      Friday 11-13    C1  


      Course Prerequisites

      Course prerequisites include knowledge of machine learning fundamentals (e.g. covered through ML course). Knowledge of elements of probability and statistics, calculus and optimization algorithms are also expected. Previous programming experience with Python is a plus for the practical lectures.

      Course Overview

      The course introduces students to the analysis and design of advanced machine learning models for modern pattern recognition problems and discusses how to realize advanced applications exploiting computational intelligence techniques.

      The course is articulated in four parts. The first part introduces basic concepts and algorithms concerning pattern recognition, in particular as pertains sequence and image analysis. The next two parts introduce advanced models from two major learning paradigms, that are (deep) neural networks and generative models, and their use in pattern recognition applications. The last part will go into the details of the realization of selected recent applications of AI models. Presentation of the theoretical models and associated algorithms will be complemented by introductory classes on the most popular software libraries used to implement them.

      The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models.

      Topics covered - Bayesian learning, graphical models, deep learning models and paradigms, deep learning for machine vision and signal processing, advanced neural network models (recurrent, recursive, etc.), (deep) reinforcement learning, signal processing and time-series analysis, image processing, filters and visual feature detectors, pattern recognition applications (machine vision, bio-informatics, robotics, medical imaging, etc), introduction to machine learning and deep learning libraries.

      Textbook and Teaching Materials

      The course does not have an official textbook covering all its contents. However, a list of reference books covering parts of the course is listed at the bottom of this section (note that all of them have an electronic version freely available online).

      [BRML] David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press

      [DL] Ian Goodfellow and Yoshua Bengio and Aaron Courville , Deep Learning, MIT Press

      • Lectures and Calendar

        The official language of the course is English: all materials, references and books are in English.

        Lecture slides will be made available here, together with suggested readings.

        Date Room Topic References Additional Material
        1 20/02/2019
        L1 Introduction to the course: motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications

        L1Signal processing: timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.

        328/02/2020C1Image processing: feature descriptors (color histograms, SIFT), spectral analysis, feature detectors (edge, blobs and segments). 

        • Course Grading and Exams

          Typical course examination (for students attending the lectures) is performed in 2 stages: midterm assignments and an oral presentation. Midterms waive the final project.

          Midterm Assignment

          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:

          • A quick and dirty (but working) implementation of a simple pattern recognition algorithm
          • A report concerning the experience of installing and running a demo application realized using available deep learning and machine learning libraries
          • A summary of a recent research paper on topics/models related to the course content.

          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, with the last one being performed the day of the oral examination. 

          Oral Exam

          The oral examination will test knowledge of the course contents (models, algorithms and applications).


          The final exam vote \(G\) is determined as

           \( G = G_O + \sum_{i=1}^{4} G_{M}^{i} \)


          • \( G_O \in [1,21] \) is the oral grade
          • \( G_{M}^i \in [0,3] \) is the grade for the i-th midterm

          Alternative Exam - Non attending students

          Working students, those not attending lectures, those who have failed or are unsatisfied with midterms can complete the course by delivering a final project and an oral exam.  Contact the instructor by mail to arrange project topics and examination dates.

          The final project concerns preparing a report 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 20 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.

          Grade for this exam modality is determined as

           \( G = 0.5 \cdot (G_P + G_O) \)

          where \( G_P \in [1,32] \) is the project grade and \( G_O \in [1,30] \) is the oral grade

          • Midterms and Projects

            • References

              Bibliographic References