GRADUATE SCHOOL
M.SC. in Computer Engineering (With Thesis)
CE 531 | Course Introduction and Application Information
| Course Name |
Machine Learning
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
CE 531
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The field of machine learning is concerned with the question of how to construct computer programs that improve automatically with experience. In recent years, many successful applications of machine learning have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundation of this field. The goal of this course is to provide an overview of the state-of-art algorithms used in machine learning. Both the theoretical properties of these algorithms and their practical applications will be discussed. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | Machine learning is concerned with computer programs that automatically improve their performance with past experiences. Machine learning draws inspiration from many fields, artificial intelligence, statistics, information theory, biology and control theory. The course will cover the following topics;concept learning,decision tree learning ,artificial neural networks , instance based learning,evolutionary algorithms ,reinforcement learning ,Bayesian learning , computational learning theory |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction | Introduction to Machine Learning, Ethem Alpaydın, Chapters 1 and 2 |
| 2 | Bayesian Decision Theory | Introduction to Machine Learning, Ethem Alpaydın, Chapter 3 |
| 3 | Parametric Methods | Introduction to Machine Learning, Ethem Alpaydın, Chapter 4 |
| 4 | Dimensionality Reduction | Introduction to Machine Learning, Ethem Alpaydın, Chapter 6 |
| 5 | Clustering | Introduction to Machine Learning, Ethem Alpaydın, Chapter 7 |
| 6 | Nonparametric Methods | Introduction to Machine Learning, Ethem Alpaydın, Chapter 8 |
| 7 | Decision Trees | Introduction to Machine Learning, Ethem Alpaydın, Chapter 9 |
| 8 | Midterm | |
| 9 | Multilayer Perceptron | Introduction to Machine Learning, Ethem Alpaydın, Chapter 11 |
| 10 | Stochastic Methods | Pattern Classification, Duda & Hart & Stork, Chapter 7 |
| 11 | Kernel Machines | Introduction to Machine Learning, Ethem Alpaydın, Chapter 14 |
| 12 | Hidden Markov Models | Introduction to Machine Learning, Ethem Alpaydın, Chapter 16 |
| 13 | Combining Multiple Learners | Introduction to Machine Learning, Ethem Alpaydın, Chapter 18 |
| 14 | Reinforcement Learning | Introduction to Machine Learning, Ethem Alpaydın, Chapter 19 |
| 15 | Semester Review | |
| 16 | Final Exam |
| Course Notes/Textbooks | Introduction to Machine Learning, Ethem Alpaydın, Fourth Edition, MIT Press, 9780262043793 |
| Suggested Readings/Materials | Pattern Classification, Richard O. Duda and Peter E. Hart and David G. Stork, Second Edition, Wiley, 9780471056690
Pattern Recognition, Sergios Theodoridis and Konstantinos Koutroumbas, Fourth Edition, Academic Press, 9781597492720
Neural Networks and Learning Machines, Simon Haykin, Third Edition, Pearson, 9780131293762
Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 9780387310732 |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
20
|
| Presentation / Jury | ||
| Project |
1
|
20
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
25
|
| Final Exam |
1
|
35
|
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
100
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
5
|
70
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
1
|
35
|
35
|
| Presentation / Jury |
0
|
||
| Project |
1
|
30
|
30
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
1
|
22
|
22
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information. |
X | ||||
| 2 | Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. | X | ||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. |
X | ||||
| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | X | ||||
| 5 | Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. |
X | ||||
| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs | X | ||||
| 7 | Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process. |
X | ||||
| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. |
X | ||||
| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
X | ||||
| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
X | ||||
| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications. |
X | ||||
| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest