Industrial and Systems 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:

Dr. Öğr. Üy. ALPER ÖNER

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)	Dimensionality Reduction
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) Acquires sufficient accumulation of knowledge in natural and applied sciences, engineering and technology, and has the ability to design, and identify/formulate/solve problems related to, complex manufacturing and service systems using this knowledge.	3	3	3	3	3
2) Possesses the ability to select and apply appropriate methods for analysing integrated systems comprising humans, knowledge, raw materials and energy; to acquire, process and interpret data; and to reach conclusions using her/his engineering skills.	2	2	2	2	2
3) Has the ability to select and efficiently use engineering design principles along with appropriate analytical, computational and experimental engineering techniques in order to optimize outputs related to various systems under realistic constraints.
4) Possesses the skills to select from among and efficiently use modern technologies, equipment, software and software languages in applications related to her/his respective field.	3	3	3	3	3
5) Possesses the ability to produce industry-focused solutions that are able to contribute to social health, safety and welfare, while being cognizant of global, cultural, societal, economical and environmental matters.	2	2	2	2	2
6) Has the awareness to take decisions ethically, professionally and without overlooking her/his legal responsibilities in situations related to her/his professions.
7) Has the awareness about contemporary issues such as sustainability, entrepreneurship and innovation; and the ability to comprehend the impacts of these notions on her/his profession.
8) Has the skills to communicate and make presentations to a level that will allow her/him to effectively make an exchange of information and experience both verbally and in written and with various communities related to her/his area.
9) Is able to use a foreign language at least at B1 level, measured in terms of the European Language Portfolio criterion.
10) In cognizance of life-long learning, possesses the ability to follow and adapt to changes that may arise in her/his field and reflect them into her/his profession.
11) Has the ability to work efficiently in interdisciplinary projects, be open to collaboration and take initiative when necessary, manage risks, plan activities and develop strategies.
12) She has the ability to follow new approaches in the field of human-machine interaction and artificial intelligence and apply them to problems in her field.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Acquires sufficient accumulation of knowledge in natural and applied sciences, engineering and technology, and has the ability to design, and identify/formulate/solve problems related to, complex manufacturing and service systems using this knowledge.	3
2)	Possesses the ability to select and apply appropriate methods for analysing integrated systems comprising humans, knowledge, raw materials and energy; to acquire, process and interpret data; and to reach conclusions using her/his engineering skills.	2
3)	Has the ability to select and efficiently use engineering design principles along with appropriate analytical, computational and experimental engineering techniques in order to optimize outputs related to various systems under realistic constraints.
4)	Possesses the skills to select from among and efficiently use modern technologies, equipment, software and software languages in applications related to her/his respective field.	3
5)	Possesses the ability to produce industry-focused solutions that are able to contribute to social health, safety and welfare, while being cognizant of global, cultural, societal, economical and environmental matters.	2
6)	Has the awareness to take decisions ethically, professionally and without overlooking her/his legal responsibilities in situations related to her/his professions.
7)	Has the awareness about contemporary issues such as sustainability, entrepreneurship and innovation; and the ability to comprehend the impacts of these notions on her/his profession.
8)	Has the skills to communicate and make presentations to a level that will allow her/him to effectively make an exchange of information and experience both verbally and in written and with various communities related to her/his area.
9)	Is able to use a foreign language at least at B1 level, measured in terms of the European Language Portfolio criterion.
10)	In cognizance of life-long learning, possesses the ability to follow and adapt to changes that may arise in her/his field and reflect them into her/his profession.
11)	Has the ability to work efficiently in interdisciplinary projects, be open to collaboration and take initiative when necessary, manage risks, plan activities and develop strategies.
12)	She has the ability to follow new approaches in the field of human-machine interaction and artificial intelligence and apply them to problems in her field.

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	Workload
Course Hours	16	48
Laboratory	16	32
Study Hours Out of Class	16	16
Presentations / Seminar	16	16
Project	16	16
Homework Assignments	16	16
Final	16	16
Total Workload		160