FACULTY OF ENGINEERING

Department of Mechatronics Engineering

CE 345 | Course Introduction and Application Information

Course Name
Introduction to Machine Learning
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 345
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
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;
  • will be able to distinguish between a range of machine learning techniques.
  • will be able to apply the basic techniques/algorithms of the field.
  • will be able to compare various techniques/algorithms of the field.
  • will be able to design various Machine Learning algorithms to specific situations.
  • will be able to evaluate potential applications of Machine Learning techniques.
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.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 1)
2 Supervised Learning E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 2)
3 Bayesian Decision Theory E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 3)
4 Dimensionality Reduction E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 6)
5 Clustering E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 7)
6 Decision Trees E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 9)
7 Linear Discrimination E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 10)
8 Midterm
9 Multilayer Perceptrons E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 11)
10 Local Models E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 12)
11 Kernel Machines E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 13)
12 Graphical Models E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 14)
13 Hidden Markov Models E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 15)
14 Reinforcement Learning E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189 (Ch. 18)
15 Semester review
16 Final Exam

 

Course Notes/Textbooks

E. Alpaydın, Introduction to Machine Learning; The MIT Press, 2014, hardcover ISBN 978-0-262-028189

Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
4
20
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
35
Final Exam
1
45
Total

Weighting of Semester Activities on the Final Grade
5
55
Weighting of End-of-Semester Activities on the Final Grade
1
45
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
4
56
Field Work
0
Quizzes / Studio Critiques
4
3
12
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
14
14
Final Exam
1
20
20
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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