COURSE INFORMATION Course Title: MACHINE LEARNING

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 878	A	2	3	0	0	3	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Ali Osman Topal , Friday: 10:00 - 12:00
Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Classroom and Meeting Time:
Course Description:	-
Course Objectives:	This course on Machine Learning will explain how to build systems that learn and adapt using real-world applications (such as robotics and brain wave signal understanding). Some of the topics to be covered include reinforcement learning, neural networks, genetic algorithms and genetic programming, parametric learning (density estimation), clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.

COURSE OUTLINE

Week	Topics
1	Introduction to Machine Learning
2	Linear Regression with one Variable
3	Multivariate Linear Regression. MATLAB: Basic operations
4	Regularizes, Learning curves
5	Optimization
6	Classification: Linear classification, logistic regression
7	Classification: Support Vector Machines
8	Kernels
9	Midterm
10	Neural Network: Representation
11	Neural Network: Learning, Backpropagation
12	Convolutional Neural Network
13	Unsupervised Learning: Clustering
14	Anomaly Detection

Prerequisite(s):	Linear Algebra
Textbook:	T. Mitchell, Machine Learning, McGraw-Hill
Other References:	C.Bishop, Pattern Recognition and Machine Learning, Springer
Laboratory Work:	2 hours
Computer Usage:	MATLAB, Python
Others:	No

COURSE LEARNING OUTCOMES

1	Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
2	Have an understanding of the strengths and weaknesses of many popular machine learning approaches.
3	Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
4	Be able to design and implement various machine learning algorithms in a range of real-world applications.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Doctorate (PhD) in Computer Engineering Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	5
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	5
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	5
8	Ability for effective oral and official communication skills in foreign language.	4
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	5
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	3
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Project	1	50
Lab/Practical Exams(s)	1	10
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	3	48
Mid-terms			0
Assignments	1	40	40
Final examination	1	25	25
Other	1	26.5	26.5
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5