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B003725 - ARTIFICIAL INTELLIGENCE
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2015-16
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
21/09/2015 ⇒ 18/12/2015
Attendance required
No
Type of Evaluation
Final Grade
Course Content
show
Course program
show
Lectureship
Teaching Language
Italian
Course Content
- Introduction and agent-based architectures.
- Searching and problem solving
- Constraint programming
- Knowledge representation, reasoning, logic
- Probabilistic graphical models
- Machine learning
Suggested readings (Search our library's catalogue)
Textbooks:
[1] Stuart Russell and Peter Norvig.Artificial Intelligence, A Modern approach (3rd revised Edition), Prentice Hall, 2010.
[2] D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012
Other texts:
[3] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, 2nd ed. Springer, 2009.
[4] John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000
[5] David L. Poole, Alan K. Mackworth. complete book is available online.
[6] Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.
[7] P. Flach. Simply Logical. Intelligent Resoning by Esample, Wiley 1994 (free download)
[8] M. Genesereth, N. J. Nilsson. Logical Foundations of Artificial Intelligence. Morgan Kaufmann Publishers, 1987. Alibris
[1] Stuart Russell and Peter Norvig.Artificial Intelligence, A Modern approach (3rd revised Edition), Prentice Hall, 2010.
[2] D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012
Other texts:
[3] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, 2nd ed. Springer, 2009.
[4] John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000
[5] David L. Poole, Alan K. Mackworth. complete book is available online.
[6] Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.
[7] P. Flach. Simply Logical. Intelligent Resoning by Esample, Wiley 1994 (free download)
[8] 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 analysis and design skills in
specific areas such as searching, constraints, logic, belief networks, and learning.
You will also gain elementary analysis and design skills in
specific areas such as searching, constraints, logic, belief networks, and learning.
Prerequisites
B003368 (Algorithms and Data Structures)
Teaching Methods
Lectures + lab sessions
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:#7d0004"> <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’>Readings/Handouts <tr style=’background:#eeebde’> <th >2015-09-23 Administrivia. Introduction to Artificial Intelligence. Intelligence and rationality. Some basic theoretical computer science notions. <td > RN10 1 <br >
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