Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
EE0848		Machine Learning for Signal Processing (Will be offered in the spring semester)	2/2/0	DE	English	6

Course Goals	Understand core machine learning principles and paradigms; implement, evaluate, and select appropriate algorithms (e.g., using Python) for real-world problem-solving; and critically analyze model results.
Prerequisite(s)
Corequisite(s)
Special Requisite(s)
Instructor(s)	Lecturer Basri Erdoğan
Course Assistant(s)
Schedule	Theory, Wednesday,11:00-12:45 Recitation, Thursday, 13:00-14:45
Office Hour(s)	Basri ERDOGAN, Monday, 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	Introduction to ML	Lecture
2. Week	Supervised Learning: Linear Regression	Lecture, practice
3. Week	Supervised Learning: Logistic Regression	Lecture, practice
4. Week	Model Evaluation and Selection	Lecture, practice
5. Week	Regularization	Lecture, practice
6. Week	Supervised Learning: Trees	Lecture, practice
7. Week	Ensemble Methods: Random Forests	Lecture, practice
8. Week	Gradient Descent and XGBoost	Lecture, practice
9. Week	Midterm
10. Week	Unsupervised Learning: Clustering	Lecture, practice
11. Week	Reinforcement Learning	Lecture, practice
12. Week	Neural Networks: Introduction	Lecture, practice
13. Week	Neural Networks: CNNs	Lecture, practice
14. Week	Transformers	Lecture, practice
15. Week
16. Week
17. Week

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	25
Quizzes	3	15
Project(s)	1	30
Final Exam	1	30

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 Machine Learning Fundamentals – Explain key ML concepts, types. LO-2 Apply Supervised Learning Techniques – Implement and evaluate regression and classification models. LO-3 Analyze Model Performance – Use metrics, cross-validation, and regularization to optimize models. LO-4 Develop Ensemble Learning Models – Implement bagging and boosting techniques for improved accuracy. LO-5 Utilize Unsupervised Learning Methods – Perform clustering on datasets. LO-6 Build and Train Neural Networks – Design basic MLP and CNN architectures for deep learning applications. LO-7 Explore Advanced ML Architectures – Understand reinforcement learning and transformer models for NLP tasks.

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
LO 1
LO 2
LO 3
LO 4
LO 5
LO 6
LO 7