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B003725 - ARTIFICIAL INTELLIGENCE
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Further information
Type of Assessment
Course program
Academic Year 2012-13
Course year
Third year - First Semester
Belonging Department
Information Engineering (DINFO)
Course Type
Single education field course
Scientific Area
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Credits
6
Teaching Hours
54
Teaching Term
17/09/2012 ⇒ 21/12/2012
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Teaching Language
Italian
Course Content
- Introduction and agent-based architectures.
- Searching and problem solving
- Constraint programming
- Knowledge representation, reasoning, logic programming.
- Machine learning
Suggested readings (Search our library's catalogue)
Textbook:
[1] Stuart Russell, Peter Norvig. Artificial Intelligence, A Modern approach (3rd Edition), Prentice Hall, 2009.
Other texts:
[2] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, 2nd ed. Springer, 2009.
[3] John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000
[4] David L. Poole, Alan K. Mackworth. complete book is available online.
[5] Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.
[6] P. Flach. Simply Logical. Intelligent Resoning by Esample, Wiley 1994 (free download)
[7] M. Genesereth, N. J. Nilsson. Logical Foundations of Artificial Intelligence. Morgan Kaufmann Publishers, 1987. Alibris
[1] Stuart Russell, Peter Norvig. Artificial Intelligence, A Modern approach (3rd Edition), Prentice Hall, 2009.
Other texts:
[2] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, 2nd ed. Springer, 2009.
[3] John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000
[4] David L. Poole, Alan K. Mackworth. complete book is available online.
[5] Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.
[6] P. Flach. Simply Logical. Intelligent Resoning by Esample, Wiley 1994 (free download)
[7] M. Genesereth, N. J. Nilsson. Logical Foundations of Artificial Intelligence. Morgan Kaufmann Publishers, 1987. Alibris
Learning Objectives
You will learn about the basic concepts and the main research areas of AI.
You will also gain elementary design skills in
specific areas such as searching, constraints, logic, and learning.
You will also gain elementary design skills in
specific areas such as searching, constraints, logic, and learning.
Prerequisites
B003368 (Algorithms and Data Structures)
Teaching Methods
Lectures + lab sessions
Further information
- Class schedule
-
See the University examinations timetable for exams dates. - Follow this link to registrer for an exam
Type of Assessment
Final project + Final oral exam.
The final project will be assigned by the teacher on one of the main topics of the course (Learning, Searching, Constraints, Logic programming).
The oral exam covers the rest of the course.
The final project will be assigned by the teacher on one of the main topics of the course (Learning, Searching, Constraints, Logic programming).
The oral exam covers the rest of the course.
Course program
<table summary="timetable" border="0" cellpadding="5" cellspacing="2"> <tr style="background:#666"> <td style='color:#fff;font-weight: bold'>Date <td style='color:#fff;font-weight: bold' width="50%">Topics <td style='color:#fff;font-weight: bold'>Readingds/Handouts <tr style='background:#eee'> <th >20/09/2012 Administrivia. Introduction to Artificial Intelligence. Intelligence and rationality. Overview of main AI areas. Agents: percepts and actions. The agent function. Environment types. Examples <td > RN09 1,2.1-2.3 <br \> Prolog code