Intelligent Systems 18/19

This web site is dedicated to the Intelligent Systems M course. The web site aims at providing the students with access to information related to the goals, contents, study material and assessment criteria of the course.

Course schedule: see the information system of the School of Engineering.

Course professor:
Prof. ssa Michela Milano, Tel. 051 20 93790, michela <dot> milano <at> unibo <dot> it
Office hourse: Thursday from 10 to 12 (ex-CSITE building, above lecture room 8.1)

Teaching Assistant:
Dott. Michele Lombardi, Tel. 051 20 93270, michele <dot> lombardi2 <at> unibo <dot> it
Office hours: Tuesday from 10 to 12, on appointment to be booked by email ("Aule Nuove" building, close to lecture room 5.7) 

Notices:

  • None, at the moment

Goals

The course takes advantage of some of the topics previously discussed in the Artificial Intelligence course (year 1), such as knowledge representation, logic, informed search strategies, game theory, constraint resolution. The Intelligent System course starts from such bases and aims at presenting the main applications of Artificial Intelligent methods, with practical examples.

The main goals of the course are:

  • Applying the techniques learned in the Artificial Intelligent course (year 1) to complex problems
  • Investigating complex problems and the main formal and algorithmic tools to address them
  • Providing practical examples

Moreover:

  • Learning how to perform a critical readout of a scientific paper concerning the topics of the course
  • Providing a practical approach to solve real problems
  • Learning how to prepare a presentation, similar to that needed for the Master Thesis
  • Giving a chanes to the students to attend seminars from researchers actively involved in advanced AI topics

Program

The course features both lectures and lab sessions. For each of the considered topics, a lab session will be scheduled after the lectures, so as to provide a chance to learn the software systems related to the presented topic. The techniques discussed in this course represent the state of the art of scientific research in Artificial Intelligence: for each of the consdidered topic, the students will be referred to survey paper that provide a good overview of state of the art research.

The detailed program is as follows:

  1. Planning
    1. Non linear planning
    2. Hierarchical planning
    3. Graph-based planning 
  2. Swarm Intelligence
    1. Ant-colony Optimization
    2. Particle Swarm Optimization
  3. Constraint Programming and Optimization
    1. Applucations
    2. Advanced Search and Propagation Strategies
  4. Machine Learning
    1. Decision Trees
    2. Neural Networks

Assessment methods

The final exams consts of a written test containing both exercises and questions about theoretical topics. For this reason, it will not be possible to bring notes and books to the test.

Optional Course Project

It is possible (by formally requesting it when planning the courses to take) to opt for a course project in Intelligent Systems. In such a case, the project topic should be defined together with the course teacher.

The project may involve using an existing system to solve a complex problem , or the development of a new tool to solve for an AI application. The student should provide:

  • A presentaion (i.e. slides) to summarize the main steps of the projec, which will serve as a basis to guide the discussion
  • The project code