Information Package
baskent.edu.tr

Master's of Computer and Instructional Technologies Education without Thesis (Distance Education)

BOTU626

ARTIFICAL INTELLIGENCE AND APPLICATIONS

Course Code
BOTU626
Semester
Second Term (Spring)
Theory+Practice
2 + 2
ECTS
6
Course TypeCompulsory Course
Course LevelMaster's Degree Without Thesis
Year of Study1
SemesterSecond Term (Spring)
ECTS6
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 DeliveryE-Learning
PrerequisitesNo
Recommended Optional Programme ComponentsNone
Course DescriptionBasic problem-solving strategies. A heuristic search principle. Problem reduction and AND/OR graphs. Expert systems and knowledge representation. An expert system shell. Planning. Language processing with grammer rules. Machine learning. Game playing. Logic and uncertainty. Meta programming.
Course Contents
WEEK TOPICS
1st Week Introduction
2nd Week Definition of Artificial Intelligence
3rd Week The Relationship Between Natural and Artificial Intelligence - AI from Past to Present
4th Week Expert and Knowledge-Based Systems
5th Week Problem-Solving Techniques
6th Week Knowledge Representation and Reasoning
7th Week Propositional Logic
8th Week Syntax of First-Order Predicate Logic
9th Week Logic Programming
10th Week Semantics
11th Week Proof Theory
12th Week Uncertainties
13th Week Probabilistic Reasoning
14th Week Use of Expert Systems in Education
Recommended or Required ReadingStuart Russel, Peter Norvig. Artificial Intelligence.Modern Approach, 2nd Ed., Prentice Hall
Yapay Zeka Problemler, Yöntemler, Algoritma, Vasıf Nabiyev, Seçkin Yay.
Introduction to Artificial Inteligence, Eugene Charniak, Drew McDermott, Addison-Wesley Pub.
Bilgisayar Destekli Eğitimde Uygulamalar, Yavuz Akpınar, Anı Yay
Planned Learning Activities and Teaching Methods
  • Lecture
  • Project
  • Practice
Assessment Methods and Criteria
Quantity Percentage (%)
Assignment 4 20
Total(%) 20
Contribution of In-term Studies to Overall Grade(%) 20
Contribution of Final Examination to Overall Grade(%) 80
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work 14 3 42
Preparation for Final exam 1 45 45
Course hours 14 1 14
Preparation for Midterm exam
Laboratory (including preparation)
Final exam 1 2 2
Homework 4 18 72
Total Work Load175
Total Work Load / 30 (h)5.83
ECTS Credit of the Course6
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