At the end of this course, the students; 1) is able to express the historical development and basic concepts related to Artificial Intelligence Applications (AI), 2) Know the basic concepts and components of Propositional Logic and First-Order Logic, to adapt them to problem solutions, 3) Know the basic concepts of Knowledge Representation and Expert Systems, 4) Know the approaches and algorithms used in Searching and Problem Solving, 5) Have general knowledge about Neural Networks, Machine Learning, Data Mining, Deep Learning, 6) Know and comprehend Value Creation and Competitive Advantage Models, 7) is able to use AIAs for the improvement of an organization's activities and the competitive advantage of the business. 8) Design, define and present an artificial intelligence application project for a business within the scope of the term project.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
The aim of this course is to gain knowledge and skills related to artificial intelligence applications in businesses.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Artificial Intelligence, Its History, and Introduction to Basic Concepts
2nd Week
Propositional Logic, First-Order Logic
3rd Week
Knowledge Representation
4th Week
Reasoning with Uncertainty, Decision Analysis, Expert Systems
5th Week
Search,and Problem Solving (Uninformed Search, Heuristic Search, etc.)
6th Week
Introduction to Advanced Topics: Neural Networks, Machine Learning, Data Mining, Deep Learning
7th Week
Introduction to Advanced Topics: Neural Networks, Machine Learning, Data Mining, Deep Learning
8th Week
Midterm
9th Week
Value Creation and Competitive Advantage Models
10th Week
Influence of Artificial Intelligence on Activities and Competitiveness of an Organization
11th Week
Model for Value Generation in Companies
12th Week
An overview
13th Week
Project Presentations and Evaluation
14th Week
Project Presentations and Evaluation
RECOMENDED OR REQUIRED READING
Richard E. Neapolitan et al. (2018). Artificial Intelligence With an Introduction to Machine Learning, 2nd Ed. CRC Press. USA. Andrzej Wodecki ( 2019).Artificial Intelligence in Value Creation. Palgrave Macmillan. Wolfgang Ertel (2017). Introduction to Artificial Intelligence, Springer-Verlag London Limited. UK. Stuart Russell and Peter Norvig. (2010).Artificial Intelligence: A Modern Approach. 3rd edition. USA.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Discussion,Questions/Answers
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
40
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 WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
1,5
1,5
Preparation for Quiz
Individual or group work
14
1
14
Preparation for Final exam
1
22
22
Course hours
13
3
39
Preparation for Midterm exam
1
22
22
Laboratory (including preparation)
13
1
13
Final exam
1
1,5
1,5
Homework
Presentation (including preperation)
1
3
3
Project
1
24
24
Report writing
1
23
23
Total Workload
163
Total Workload / 30
5,43
ECTS Credits of the Course
5
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
