Academic Year 2017/2018 - 1° Year - Curriculum Data Science
Teaching Staff Credit Value: 9
Scientific field: INF/01 - Informatics
Taught classes: 36 hours
Exercise: 24 hours
Laboratories: 12 hours
Term / Semester:

Learning Objectives

  • Artificial Intelligence

    Knowledge and understanding: Students will acquire basic knowledge about Intelligent Agents and their main features.
    Applying knowledge and understanding: students will be to able to apply the acquired knowledge in several fields such as: searching for solutions to hard combinatorial problems, games and decision theory, automated deduction and reasoning.
    Autonomia di giudizio (making judgements): Students will be able to evaluate the possibility of developing algorithms and intelligent systems to mechanize decisional processes in different application fields.
    Communication skills: students will acquire the necessary communication skills and appropriate linguistic skills to explain and clarify problems relative to intelligent systems and their applications.
    Capacità di apprendimento (learning skills): students will be able to adapt the acquire knowledge to new contexts as well and to understand the limits of applicability of artificial intelligence techniques

  • AI Lab

    The AI Lab will focus on the design and implementation of algorithms on problem solving, and in particular computationally hard problems.

    At the end of the Lab, students will acquire a good experience on the implementation of specific AI search methodologies and algorithms such as

    1. Uninformed Search
    2. Informed Search and Heuristics
    3. Constraint Satisfaction problems
    4. Foundations of natural computation

Detailed Course Content

  • Artificial Intelligence

    The course is divided into 2 main parts. First part on Problem Solving, and second part on Knowledge and Reasoning.

    Contenuti dettagliati del Corso:

    FIRST PART: Problem Solving

    • Foundations and history of Artificial Intelligence
    • Intelligent Agents and classifications
    • Search and Problem Solving
    • Search in games
    • Constraint Satisfaction Problems
    • Search using Natural Computing Algorithms
    • SECOND PART: Knowledge and Reasoning

    • Logical agents and puzzles
    • First order logic
    • Inferences
    • Utility and value of information
    • Simple and Complex decision making

Textbook Information

  • Artificial Intelligence

    Required textbook is Artificial Intelligence, a modern approach, 3rd Edition, S. Russel, P. Norvig. Other material will be provided by the instructor in class.

  • AI Lab
    1. Artificial Intelligence, a modern approach, 3rdEdition, S. Russel, P. Norvig,
    2. Other material will be provided by the instructor in class.