Information Package
baskent.edu.tr

Master's of Computer and Instructional Technologies Education with Thesis

BÖTE626

ARTIFICIAL INTELLIGENCE AND APPLICATIONS

Course Code
BÖTE626
Semester
-
Theory+Practice
2 + 2
ECTS
5
Course TypeElective Course
Course LevelMaster's Degree With Thesis
Year of Study-
Semester-
ECTS5
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Will be able to learn concept of artificial intelligence and its application areas,
  2. Will be able to learn the structure and components of expert systems,
  3. Will be able to learn the characteristic of expert systems,
  4. Will be able to learn the design principals and components needed to use expert systems in education,
  5. Will be able to learn intelligent learning systems and their components,
  6. Will able to design for intelligent learning systems,
  7. Will be able to learn the characteristics of logical programming languages,
  8. Will be able to use a logical programming language at basic level.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme ComponentsNone
Course DescriptionThe primiry 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. In this course, the most fundamental knowledge for understanding AI will be studied. Some basic search algorithms for problem solving, knowledge representation and reasoning, pattern recognition, fuzzy logic and neural networks will be introduced.
Course Contents
WEEK TOPICS
1st Week Introduction to course
2nd Week Introduction to AI
3rd Week Problem formulation
4th Week Search ? I & II
5th Week Production system
6th Week Ontology Propositional logic
7th Week First order predicate logic
8th Week Fuzzy logic
9th Week Pattern Recognition
10th Week Self-Organizing Neural Networks
11th Week Multilayer Neural Networks
12th Week Decision trees
13th Week Population-based search
Recommended or Required Reading1) Stuart Russel, Peter Norvig. Artificial Intelligence.Modern Approach, 2nd Ed., Prentice Hall
2) Yapay Zeka Problemler, Yöntemler, Algoritma, Vasıf Nabiyev, Seçkin Yay.
3) Introduction to Artificial Inteligence, Eugene Charniak, Drew McDermott, Addison-Wesley Pub.
4) Bilgisayar Destekli Eğitimde Uygulamalar, Yavuz Akpınar, Anı Yay.
5) Artificial Intelligence: a modern approach, S. Russell and P. Norvig, Prentice Hall.
Planned Learning Activities and Teaching Methods
  • Lecture
  • Project
  • Practice
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Assignment 2 10
Project 1 20
Total(%) 60
Contribution of In-term Studies to Overall Grade(%) 60
Contribution of Final Examination to Overall Grade(%) 40
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam 1 2 2
Preparation for Quiz
Individual or group work
Preparation for Final exam 1 25 25
Course hours 14 4 56
Preparation for Midterm exam 1 20 20
Laboratory (including preparation)
Final exam 1 2 2
Homework 2 15 30
Project 1 20 20
Total Work Load155
Total Work Load / 30 (h)5.16
ECTS Credit of the Course5
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
K1
K2 X X X X
K3 X
K4 X X X
K5 X
K6
K7 X
K8 X
K9 X
K10 X
K11
K12 X
K13 X X X
K14 X X X
K15 X X X X X X X X
K16 X
K17
K18 X