Computer Engineering (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

COE305

Course Name:

Machine Learning

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Compulsory Courses

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Doç. Dr. AMIR SEYYEDABBASI

Course Lecturer(s):

Assist. Prof. Dr. Femilda Josephin Joseph Shobana Bai

Course Assistants:

Course Objective and Content

Course Objectives:

This course will cover the fundamentals of Machine Learning, including supervised and unsupervised learning, classification, regression, clustering, decision trees, and basics of deep learning. Students will learn how to apply these techniques to solve real-world problems, such as image classification, natural language processing, and recommender systems. Additionally, the course will cover the concepts of model evaluation, feature selection, and data preprocessing, which are crucial for developing effective Machine Learning models.

Course Content:

The content of the course consists of linear regression, logistic regression, K-Nearest Neighbor, Support Vector Machines, Naïve Bayes, K- means clustering, Principal component analysis, ensemble learning algorithms like the random forest, and gradient boosting machines, artificial neural networks with single-layer and multiple-layer perceptrons

Learning Outcomes

The students who have succeeded in this course;
1) Understand the principles and techniques of supervised and unsupervised learning, classification, regression, clustering, decision trees, and basics of deep learning.
2) Apply Machine Learning techniques to solve real-world problems, such as image classification, natural language processing, and recommender systems.
3) Evaluate and select appropriate models for given problem statements using the concepts of model evaluation and feature selection.
4) Preprocess data to prepare it for Machine Learning modeling.
5) Analyze and interpret Machine Learning model outputs to draw insights and make informed decisions.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to Machine Learning
2)	Supervised Learning-Regresion Problem
3)	Supervised Learning - Classification Problem - Logistic Regression - K-Nearest Neighbor
4)	Exploratory Data Analysis Data Pre-processing
5)	Supervised Learning - Decision Tree - Handling imbalanced dataset
6)	Supervised Learning - Random Forest - Cross Validation and its types
7)	Supervised Learning - Naive Bayes and Support Vector Machines
8)	Midterm Exam
9)	Supervised Learning - Hyper parameter tuning Performance metrics
10)	Unsupervised Learning
11)	Ensemble Learning Methods
12)	Dimensionality Reduction
13)	Artificial Neural Networks Perceptrons
14)	Artificial Neural Networks - Multi-layer Networks

Sources

Course Notes / Textbooks:

1) Introduction to Machine Learning, Ethem Alpaydın (3rd Edition), 2014, MIT Press
2) Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz, Shai Ben-David (1st Edition), 2014, Cambridge University Press
3) Pattern Recognition and Machine Learning, Christopher Bishop (1st Edition), 2006, Springer
4) Learning from Data, Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin (1st Edition), 2012, AMLBook

References:

"1) Linear Algebra and Optimization for Machine Learning: A Textbook, Charu C. Aggarwal (1st Edition), 2020, Springer Press
2) Optimization for Machine Learning, Suvrit Sra, Sebastian Nowozin, Stephen J. Wright (Edited - 1st Edition), 2011, MIT Press"

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Adequate knowledge in mathematics, science, and computer engineering principles, both theoretical and practical, and the ability to apply this knowledge to complex engineering problems.	2	2	2	2	2
2) Ability to identify, formulate, and solve complex computer engineering problems using appropriate analysis and modeling techniques.	2	2	2	2	2
3) Ability to design and develop complex computer systems, devices, or products that meet specific requirements and operate under realistic constraints and conditions, using modern design methods.
4) Ability to develop, select and use modern techniques and tools used for the analysis and solution of complex computer engineering problems, and the ability to use information technologies effectively.	2	2	2	2	2
5) Ability to plan and conduct experiments, collect and analyze data, and interpret results in the study of complex computer engineering problems or research topics.	2	2	2	2	2
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of computer engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in computer engineering; awareness of the legal consequences of computer engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science, and computer engineering principles, both theoretical and practical, and the ability to apply this knowledge to complex engineering problems.	2
2)	Ability to identify, formulate, and solve complex computer engineering problems using appropriate analysis and modeling techniques.	2
3)	Ability to design and develop complex computer systems, devices, or products that meet specific requirements and operate under realistic constraints and conditions, using modern design methods.
4)	Ability to develop, select and use modern techniques and tools used for the analysis and solution of complex computer engineering problems, and the ability to use information technologies effectively.	2
5)	Ability to plan and conduct experiments, collect and analyze data, and interpret results in the study of complex computer engineering problems or research topics.	2
6)	Ability to work effectively within and multidisciplinary teams; individual study skills.
7)	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of computer engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in computer engineering; awareness of the legal consequences of computer engineering solutions.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 10
Homework Assignments	2	% 10
Project	1	% 10
Midterms	1	% 30
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	13	5			65
Application	1	5			5
Project	1	4	4		8
Homework Assignments	2	12			24
Quizzes	2	8			16
Midterms	1	7			7
Final	1	13			13
Total Workload					138