CMP4501 Introduction to Artificial Intelligence and Expert SystemsBahçeşehir UniversityDegree Programs INDUSTRIAL DESIGNGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
INDUSTRIAL DESIGN
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP4501 Introduction to Artificial Intelligence and Expert Systems Fall 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi TEVFİK AYTEKİN
Recommended Optional Program Components: None
Course Objectives: The course introduces basics of artificial intelligence. Basic search techniques used for problem solving, fundamentals of knowledge representation and logical formalisms, basic learning algorithms, and fundamentals of expert systems will be introduced.

Learning Outcomes

The students who have succeeded in this course;
I. Be able to formulate a state space description of a problem
II. Be able to select and implement brute-force or heuristic algorithm for a problem.
III. Be able to implement minimax search with alpha-beta pruning.
IV. Be able to compare and evaluate the most common models for knowledge representation.
V. Be able to explain the operation of the resolution technique for theorem proving.
VI.Be able to explain the differences among supervised and unsupervised learning.
VII. Be able to explain the concepts of overfitting, underfitting, bias, and variance.
VIII. Be able to implement some of the basic algorithms for supervised learning and unsupervised learning.
IX. Be able to describe fundamentals of expert systems and evaluate them.

Course Content

Introduction to AI, state spaces and searching, heuristic functions and search, alpha-beta pruning, propositional and first-order predicate logic, propositional and first order inference, unification and resolution, linear regression, logistic regression, neural networks and backpropagation algorithm, Bayes’ rule and naive Bayes algorithm, clustering and k-means algorithm, fundementals of expert systems, software for expert systems.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to AI
2) State spaces and searching.
3) Heuristic functions and search
4) Decisions in games, alpha-beta pruning.
5) Propositional and first-order predicate logic
6) Propositional and first order inference
7) Unification and resolution
8) Linear Regression
9) Midterm
10) Logistic Regression
11) Neural networks and backpropagation algorithm.
12) Bayes’s rule and naive Bayes algorithm.
13) Clustering and k-means algorithm
14) Fundementals of expert systems.
15) Software for expert systems.

Sources

Course Notes / Textbooks: Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009.

Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004.
References: Yok - None

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Quizzes 2 % 10
Project 1 % 20
Midterms 1 % 30
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Project 4 20
Homework Assignments 10 20
Quizzes 2 8
Midterms 5 15
Final 5 20
Total Workload 125

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution