Identification
Prerequisites
Language
Compulsory/Elective
Required textbooks
and course materials
Course website
Course outline
Course objectives
Learning outcomes
CMS 415 – Artificial Intelligence 3 credits
Computer Science
Undergraduate
Spring, 2015
Associate Professor Leyla Muradkhanli
leyla@khazar.org
(+994 12) 421-10-93 (ext. 227)
11 Mehseti str. (Neftchilar campus), Room #401N, Monday
18:10-21:00
Monday, 17:00 – 18:00 or by appointment
Office hours
CMS 316 Database Management Systems
English
Required
Core textbook:
Artificial intelligence : a modern approach / Stuart J. Russell and Peter Norvig ;
3rd edition, Prentice Hall, 2010.
Supplementary textbook:
George F.Luger Artificial Intelligence. Structures and Strategies for complex
problem solving. Sixth edition, Pearson Addison Wesley, 2009.
Subject
Department
Program
Term
Instructor
E-mail:
Phone:
Classroom/hours
This course combines traditional face-to-face classes with online learning. The course
management platform Moodle is used to provide a wide range of resources to support
learning. And all course related materials including, but not limited to, syllabus,
supplementary readings, course announcements, cases and assignments are available
only at the course website http://www.khazar.org/moodle. Grades will also be posted
on Moodle. The students are expected to check it n a regular basis and communicate
with the lecturer only via Moodle.
This course provides an introduction to the tools, techniques and concepts of Artificial
Intelligence. The course combines theoretical foundations with practical applications.
Topics include: problem solving, principles of knowledge representation and
reasoning, and learning methods of artificial intelligence.
Generic Objective of the Course:
To develop an understanding of the basic concepts of Artificial Intelligence.
Specific Objectives of the Course:
 To introduce students artificial intelligence tools and techniques;
 To develop skills thorough understanding of the theoretical and practical aspects
of Artificial Intelligence;
 To support the students academically, to improve their chance of realizing their
potential;
 To encourage students participation and interaction and fostering atmosphere of
tolerance and respect.
By the end of the course the students should be able to :

Teaching methods
Understand the role of knowledge representation, problem solving, and
learning methods of artificial intelligence;
 Recognize problems that may be solved using artificial intelligence;
 Acquire working knowledge of several popular knowledge based techniques;
 Implement artificial intelligence algorithms to solve a variety of problems.
x
Lecture
x
Group discussion
x
Experiential exercise
x
Simulation
Case analysis
x
Course paper
Others
Evaluation
Policy
Methods
Midterm Exam
Case studies
Class Participation
Assignment and
quizzes
Project
Presentation/Group
Discussion
Final Exam
Others
Total
 Preparation for class
Date/deadlines
Percentage (%)
30
20
15
35
100
The structure of this course makes your individual study and preparation outside
the class extremely important. The lecture material will focus on the major points
introduced in the text. Reading the assigned chapters and having some familiarity
with them before class will greatly assist your understanding of the lecture. After
the lecture, you should study your notes and work relevant problems and cases
from the end of the chapter and sample exam questions.
Throughout the semester we will also have a large number of review sessions.
These review sessions will take place during the regularly scheduled class
periods.

Withdrawal (pass/fail)
This course strictly follows grading policy of the School of Engineering and
Applied Science. Thus, a student is normally expected to achieve a mark of at
least 60% to pass. In case of failure, he/she will be required to repeat the course
the following term or year.

Cheating/plagiarism
Cheating or other plagiarism during the Quizzes, Mid-term and Final
Examinations will lead to paper cancellation. In this case, the student will
automatically get zero (0), without any considerations.

Professional behavior guidelines
The students shall behave in the way to create favorable academic and
professional environment during the class hours. Unauthorized discussions and
unethical behavior are strictly prohibited.

Ethics
We
ek
Students should not arrive in late to class. All cell phones and personal electronic
devices must be turned off and stowed away before entering class.
Date/Day
(tentative)
1
26.01.15
Tentative Schedule
Topics
Basic of Artificial Intelligence
The nature of Intelligence.
The difference between Natural and Artificial Intelligence.
History of Artificial Intelligence.
Textbook/Assignments
Ch.1
2
3
4
5
6
02.02.15
09.02.15
16.02.15
23.02.15
02.03.15
16.03.15
7
Intelligent agents
Agents and environments
Nature of environments
Structure of agents
Solving Problems by Searching
Problem-solving agents
Searching for solutions
Uninformed search strategies
Solving Problems by Searching
Informed (heuristic) search strategies
Heuristic functions
Game playing and adversarial search
Optimal decisions in games
Imperfect real time decisions
Stochastic games
Partially observable games
Logical Agents
Knowledge-based agents
Logic
Propositional logic
Agents based on propositional logic
First-Order Logic
Syntax and semantics of first-order logic
Knowledge engineering in first-order logic
8
30.03.15
Midterm Exam
9
06.04.15
Inference in First-Order Logic
Propositional vs first-order inference
Unification and lifting
Forward chaining
Backward chaining
Resolution
10
11
12
13
13.04.15
20.04.15
27.04.15
04.05.15
Knowledge Representation
Uncertain Knowledge
Probabilistic Reasoning
Machine Learning
Learning from examples
Supervised learning
Artificial Neural Networks
Reinforcement Learning
Natural Language Processing
Language models
Text classification
Information retrieval
Information extraction
Natural Language for Communication
Phrase structure grammars
Ch.2
Ch.3
Ch.3
Ch.5
Ch.7
Ch.8
Ch.9
Ch 12
Ch.13
Ch.14
Ch.18
Ch.21
Ch.22
Ch.23
Syntactic analysis (parsing)
Augmented grammars and semantic interpretation
Machine translation
Speech recognition
14
15
11.05.15
18.05.15
TBA
Computer Vision
Image formation
Object recognition by appearance
Object recognition form structural information
Using vision
Robotics
Introduction
Robot hardware
Robotic perception
Planning to move
Planning uncertain movements
Moving
Robotic software architectures
Final Exam
Ch.24
Ch.25