Facebook pixel
Info
Foto sezione
Logo Bocconi

Course 2022-2023 a.y.

30592 - TOPICS IN COMPUTATIONAL MODELLING: FROM INFORMATION THEORY TO EVOLUTIONARY MODELS

Department of Computing Sciences

Course taught in English

Go to class group/s: 31

CLEAM (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - CLEF (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - CLEACC (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - BESS-CLES (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - WBB (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - BIEF (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - BIEM (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - BIG (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - BEMACS (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01) - BAI (6 credits - II sem. - OP  |  3 credits FIS/02  |  3 credits INF/01)
Course Director:
FRANCESCA BUFFA

Classes: 31 (II sem.)
Instructors:
Class 31: FRANCESCA BUFFA


Suggested background knowledge

For a fruitful and effective learning experience, it is recommended a preliminary knowledge of basic linear algebra, elements of probability and statistics, calculus and differential equations, optimization. Some facility in at least one programming language and mathematical modelling, and basic understanding of Machine Learning or Statistical Learning approaches would be an advantage.


Mission & Content Summary
MISSION

The purpose of this course will be to introduce the students to the mathematics of information theory and its applications, including evolutionary modelling. We will explore evolution as an abstract adaptive process of improvement dependent upon replication with variation and selection, and we will examine its role in learning and Artificial Intelligence.

CONTENT SUMMARY

▪  Introduction to Information Theory

▪  Review of information measures 

▪  Codes and compression 

▪  Information and evolution

▪  Evolutionary models

▪  Role of evolution in learning

▪  Genetics algorithms

▪  Evolutionary computation

▪  Intelligent Agents


Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...

▪  State and use the basic concepts in information theory, and their applications

▪  Discuss evolution as a framework for learning, and its connection with Artificial Intelligence 

▪  Describe the basic ideas of evolutionary computation, and the main methodologies 

 

 

APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...

▪  Demonstrate the ability to recognize evolution in real-world situations

▪  Use conceptual ideas of evolution theory in problem solving

▪  Apply evolutionary models to power intelligent agents 


Teaching methods
  • Face-to-face lectures
  • Individual assignments
  • Group assignments
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)
DETAILS
  • Individual assignements: each student will be required to apply evolutionary computation to a real-world problem, and provide a written report and a code.
  • Groups assigment/Interactive class activities: students will be divided in groups and will research on the role that evolution plays in learning, recent developments and further potential. Groups are expected to present and discuss their findings with the class (questions will be provided as a guide), and provide a written essay summarizing their ideas. 

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS

    The exam consists of a theory part and problem solving projects.

    • The theory part consists in exercises and questions to be answered on paper, and is used to asses the "knowledge and understanding" learning   objectives. This contributes to 50% of the final grade.
    • The project consists in a code that uses evolutionary programming to solve a real-world problem, to be developed individually and described through a written report, which is evaluated by the teachers. This contributes to 30% of the final grade.
    • An interactive activity between working groups is organized. This contributes to the remaining 20% of the final grade, and will be marked as group work (same mark to all members of the group).

    The projects are used to asses the "applying knowledge and understanding" learning   objectives. In order to pass the exam, students must achieve a passing grade in both the theory part and the projects part.


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    T. Cover, and J. Thomas. Elements of Information Theory, Second Edition. Wiley-Interscience, 2006. ISBN: 9780471241959.

    A.E. Eiben, J.E. Smith. Introduction to Evolutionary Computing. Springer; 2nd ed. 2015. ‎ ISBN: 9783662448731 

     

    Additional teaching material will be suggested during the course

    Last change 31/05/2022 16:26