Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
EE0848		Machine Learning for Signal Processing	2/2/0	DE	English	6

Course Goals	The objective of this course is to equip students with a comprehensive understanding of fundamental machine learning concepts and techniques, enabling them to identify various machine learning categories, implement algorithms like k-Nearest Neighbors and linear regression, apply essential data processing techniques, and evaluate models using advanced metrics and validation methods.
Prerequisite(s)
Corequisite(s)
Special Requisite(s)
Instructor(s)	Lecturer Basri Erdoğan
Course Assistant(s)
Schedule	Theory, Friday,13:00-14:45 Recitation, Friday, 15:00-16:45
Office Hour(s)	Basri ERDOGAN, Friday, 11-12:00, 2D-07, Atakoy Campus
Teaching Methods and Techniques	- Lecture in classroom - Practice in computer lab
Principle Sources	- Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python, O’Reilly - Sebastian Raschka, Vahid Mirjalili, Python Machine Learning Third Edition, Packt Publishing Ltd. - Wes McKinney, Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython, O’Reilly
Other Sources	- https://scikit-learn.org/stable/user_guide.html - https://pytorch.org/tutorials/

Course Schedules
Week	Contents	Learning Methods
1. Week	Coure introduction	Lecture
2. Week	What is Machine learning and what are the main categories.	Lecture, practice
3. Week	k-Nearest Neighbors algorithm	Lecture, practice
4. Week	Data preprocessing and an introduction to scikit-learn toolbox	Lecture, practice
5. Week	Random sampling, scaling and working with categorical data	Lecture, practice
6. Week	Cross validation, Hyperparameter search	Lecture, practice
7. Week	Linear regression and regularization	Lecture, practice
8. Week	Midterm
9. Week	Linear models for classification	Lecture, practice
10. Week	Tree based models (Decision trees and Random forests)	Lecture, practice
11. Week	Gradient descent, XGBoost	Lecture, practice
12. Week	Model evaluation	Lecture, practice
13. Week	Neural networks	Lecture, practice
14. Week	Review	Lecture, practice
15. Week
16. Week
17. Week

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	25
Homework / Term Projects / Presentations	1	15
Project(s)	1	25
Final Exam	1	35

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 modeling 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, analyze 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 Understand the k-Nearest Neighbors (k-NN) algorithm and its basic principles LO-2 Apply processing techniques such as feature scaling and normalization LO-3 Understand how to handle categorical data in machine learning LO-4 Understand and apply cross validation for model evaluation. LO-5 Implement linear regression with regularization using Scikit-learn LO-6 Understand the principles of linear models for classification LO-7 Understand the principles of decision trees and random forests LO-8 Use various techniques such as confusion matrix, ROC curve, and precision-recall curve for model evaluation LO-9 Identify the main categories of machine learning and their characteristics

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11