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This document provides information about an Artificial Intelligence course, including instructor details, course structure, learning outcomes, and textbook information. The course covers fundamental AI topics like search algorithms, knowledge representation, reasoning, and machine learning over 30 lecture hours and 15 practical work hours. It is divided into 4 chapters covering logic foundations, problem solving techniques, expert systems, and Prolog programming. The overall goal is for students to understand basic AI concepts and be able to apply algorithms to problem solving after completing the course.

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32 views3 pages

Instructor

This document provides information about an Artificial Intelligence course, including instructor details, course structure, learning outcomes, and textbook information. The course covers fundamental AI topics like search algorithms, knowledge representation, reasoning, and machine learning over 30 lecture hours and 15 practical work hours. It is divided into 4 chapters covering logic foundations, problem solving techniques, expert systems, and Prolog programming. The overall goal is for students to understand basic AI concepts and be able to apply algorithms to problem solving after completing the course.

Uploaded by

informatiquehageryahoo.fr
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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Course Syllabus

IA: Artificial intelligence

Academic Year(2023-2024)-First Semester

Instructor:

Name: Dr.Ben fradj HAJER

Contact inf: informatiquehager@yahoo.fr

Phone: 28983214

Course Information:

Name: Artificial intelligence

Course ID: code U.E.2.7.3

Cr.hours: 30 Integrate course (h), 15 Pratical work (h)

Course Prerequisites:
We will have regular assignments that expect you to be able to read and write Scheme. This is the
only formal pre-requisite.

Classroom Location and Time:


Chapter Title Duration (h) Learning outcomes

Chapter 1 Logic foundation 6h Predicate logic


Proposition logic

Chapter 2 IA: 12h - Problem formulation


Search-based problem - width first
solving - depth of approach
- limited depth
- iterative limited depth
- best-first search
- hill climbing
- A* algorithm, heuristics
- beam search
- simulated annealing search
- Constraint satisfaction and search (CSP)
- Strategic games and search: min-max and
alpha-beta

Chapter 3 Expert systems 12h - Knowledge base: fact base, rule base
- Inference: forward, backward and mixed
chaining

Chapter 4 Prologue 30h Basic concepts


Relationships
Solving constraint systems
Trees, tuples, strings and lists
Numerical constraints
Predefined rules and external procedures

Text Books:
Murphy, K. P. Machine Learning: A Probabilistic Perspective. (2020).
Sutton, R., Barto, A. Reinforcement Learning: An Introduction. (2018).

References:
Russell, S., Norvig, P. Intelligence artificielle : Foundations of Artificial Intelligence. (2016).
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning. (2016).

Course description
Artificial intelligence (AI) is a research field that studies how to realize the intelligent human
behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan,
and solve problems autonomously. Although AI has been studied for more than half a century,
we still cannot make a computer that is as intelligent as a human in all aspects. However, we do
have many successful applications. In some cases, the computer equipped with AI technology
can be even more intelligent than us. The Deep Blue system which defeated the world chess
champion is a well-know example.
The main research topics in AI include: problem solving, reasoning, planning, natural
language understanding, computer vision, automatic programming, machine learning, and so
on. Of course, these topics are closely related with each other. For example, the knowledge
acquired through learning can be used both for problem solving and for reasoning. In fact, the
skill for problem solving itself should be acquired through learning. Also, methods for problem
solving are useful both for reasoning and planning. Further, both natural language
understanding and computer vision can be solved using methods developed in the field of
pattern recognition.
In this course, we will study the most fundamental knowledge for understanding AI. We will
introduce some basic search algorithms for problem solving; knowledge representation and
reasoning; pattern recognition; fuzzy logic; and neural networks.

Course Learning Outcome:


After completing the course, the student shall be able to:
1. describe mile stones of AI and relate them to computer science as well as other fields
2. implement software that can use most common AI-problems
3. define the size and characteristics of a search space for a given problem and suggest suitable AI
algorithm and representation
4. successfully apply AI algorithms to problem solving

Makeup:

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