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-1
Adequate 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-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability 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-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition 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-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge 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-1
Ability to understand, interpret and analyze the solutions of machine learning applications and problems.
LO-2
Understanding how machine learning methods work on different types of data.
LO-3
Implementing machine learning algorithms.
LO-4
Ability to independently pursue a given machine learning project by understanding the optimal algorithm for solving a particular problem.