Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
COGNITION AND MACHINE LEARNING 805100715061 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals The aim of this course is to develop understanding from different perspectives on modeling machine learning by acting from learning models. The concept of learning will be studied in depth, both in terms of cognitive and modeling cognitive learning on the basis of hardware and software.
Course Content Concept Learning and Categorization, Introduction to Machine Learning, Machine Learning in a nutshell, Designing a ML program, Induction learning, Decision Trees, Rule learning, Linear models, Issues in ML design, Perceptrons and Artificial Neural Networks, Probabilistic reasoning, Bayesian Learning, Unsupervised Learning, Deep Learning
Learning Outcomes 1) Understands machine learning and its applications as a research methodology at the intersection between natural and artificial cognitive systems.
2) Discusses fundamental topics in machine learning, including supervised learning, Bayesian decision theory, decision trees, and multilayer perceptrons.
3) Applies machine learning algorithms in subdomains of cognitive science, vision and models of human learning.
4) Gains the ability to models human learning into machine learning.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Concept Learning and Classification Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
2. Week Introduction to Machine Learning Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
3. Week Machine Learning in Brief Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
4. Week Designing a BC program Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
5. Week Induction learning Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
6. Week Decision Trees Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
7. Week Learning rule Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
8. Week Linear models Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
9. Week Design problems in BC Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
10. Week Sensors and Artificial Neural Networks Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
11. Week Probabilistic reasoning Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
12. Week Bayesian Learning Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
13. Week Unsupervised Learning Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
14. Week Deep Learning Problem Solving
Brainstorming
Problem Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)

Sources Used in This Course
Recommended Sources
Lan Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Alpaydin, E. (2010). Introduction to machine learning, Second edition. Cambridge: MIT Press.
Halpern, J. Y. (2017). Reasoning about Uncertainty. The MIT Press).
Kumar, R. (2017). Machine Learning and Cognition in Enterprises: Business Intelligence Transformed. Apres.
Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux.
Mitchell, T. (1997). Machine learning. McGraw Hill.
Russell, S. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach, Third edition. Prentice Hall, NJ.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques, Third edition. Morgan Kaufman.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 3
Homework 14 3
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 2
Time to prepare for Final Exam 1 50
2 20
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload