Undergraduate
Faculty of Engineering and Architecture
Computer Engineering
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Machine Learning

Course CodeSemester Course Name LE/RC/LA Course Type Language of Instruction ECTS
CSE0443 Machine Learning 2/0/2 DE 6
Course Goals
This course aims to introduce the fundamental issues of machine learning, to provide students with a broad understanding of the algorithms developed to address different machine learning tasks and apply them to real-world problems.
Prerequisite(s) There is no prerequisite for the course.
Corequisite(s) There is no corequisite for the course.
Special Requisite(s) Introduction to Probability Theory and Statistics, Linear Algebra
Instructor(s) Doç Dr. Can EYÜPOĞLU
Course Assistant(s) There is no assistant for the course.
Schedule Theory: Thursday (11:00-12:45), Practical Group 1: Thursday (13:00-14:45) Practical Group 2: Thursday (15:00-16:45)
Office Hour(s) Thursday 10:00-11:00
Teaching Methods and Techniques Theoretical lectures, laboratory exercises, projects, readings (journal and conference papers) and discussions.
Principle Sources

Introduction to Machine Learning, Ethem Alpaydın, Boğaziçi University, Publisher: The MIT Press, 2014 (Third Edition).   

o   http://mitpress.mit.edu/9780262028189/

o   https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/


Machine Learning, Tom Mitchell, Publisher: McGraw Hill, 1997 (First Edition).

o   http://www.cs.cmu.edu/~tom/mlbook.html

o   http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html

 

Other Sources

Introduction to Data Mining, Pang-Ning Tan, Michigan State University, Michael Steinbach, University of Minnesota, Vipin Kumar, University of Minnesota, Publisher: Pearson, 2018 (Second Edition). 

o   https://www-users.cs.umn.edu/~kumar001/dmbook/index.php

Course Schedules
Week Contents Learning Methods
1. Week Overview Oral Presentation, Laboratory
2. Week Introduction to Machine Learning Oral Presentation, Laboratory
3. Week Data Splitting Oral Presentation, Laboratory
4. Week Evaluation of Machine Learning Algorithms Oral Presentation, Laboratory
5. Week Linear Regression Oral Presentation, Laboratory
6. Week Decision Trees Oral Presentation, Laboratory
7. Week Decision Trees Oral Presentation, Laboratory
8. Week Midterm
9. Week Naïve Bayes Oral Presentation, Laboratory
10. Week k-Nearest Neighbors Oral Presentation, Laboratory
11. Week Support Vector Machines Oral Presentation, Laboratory
12. Week Support Vector Machines Oral Presentation, Laboratory
13. Week Artificial Neural Networks Oral Presentation, Laboratory
14. Week Review for Final Exam Oral Presentation, Laboratory
15. Week
16. Week
17. Week
Assessments
Evaluation tools Quantity Weight(%)
Midterm(s) 1 30
Quizzes 7 10
Project(s) 1 20
Final Exam 1 40


Program Outcomes
PO-1Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9Awareness of professional and ethical responsibility.
PO-10Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1Ability to understand, interpret and analyze the solutions of machine learning applications and problems.
LO-2Understanding how machine learning methods work on different types of data.
LO-3Implementing machine learning algorithms.
LO-4Ability to independently pursue a given machine learning project by understanding the optimal algorithm for solving a particular problem.
Course Assessment Matrix:
Program Outcomes - Learning Outcomes Matrix
 PO 1PO 2PO 3PO 4PO 5PO 6PO 7PO 8PO 9PO 10PO 11
LO 1
LO 2
LO 3
LO 4