ACADEMICS
Course Details
ELE489 - Fundamentals of Machine Learning
2024-2025 Fall term information
The course is not open this term
ELE489 - Fundamentals of Machine Learning
| Program | Theoretýcal hours | Practical hours | Local credit | ECTS credit |
| Undergraduate | 3 | 2 | 4 | 7 |
| Obligation | : | Elective |
| Prerequisite courses | : | ELE320 |
| Concurrent courses | : | - |
| Delivery modes | : | Face-to-Face |
| Learning and teaching strategies | : | Lecture, Question and Answer, Problem Solving, Programming |
| Course objective | : | This course provides an introduction to the theory and applications of machine learning. The goal is to provide students with a deep understanding of the subject matter and skills to apply these concepts to real world problems using Python as the programming language. |
| Learning outcomes | : | A student who completes the course successfully has both theoretical and practical knowledge on machine learning, and he/she can apply her knowledge to real-world problems. She knows about classification, regression, clustering, supervised and unsupervised techniques in handling data, and can select between several models depending on performance metrics. |
| Course content | : | Introduction to Machine Learning, Machine Learning Tools and Libraries in the Python Programming Language, k-Nearest Neighbors, Naïve Bayes Classifier, Maximum Likelihood Estimation, Decision Trees, Support Vector Machines, Perceptron, Neural Networks, Deep Learning, Auto-encoding and Self-supervision, Generative Adversarial Networks, Classification Performance Metrics, Model Selection, Dimension Reduction, Clustering, Regression, Ensemble Methods. |
| References | : | Ethem Alpaydin, Introduction to Machine Learning, The MIT Press, 2020 Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. Edward Raff, Inside Deep Learning, Manning, 2022 |
| Weeks | Topics |
|---|---|
| 1 | Introduction to Machine Learning |
| 2 | Introduction to Python Programming: Machine Learning Tools and Libraries |
| 3 | Nearest Neighbor Classifier, Naïve Bayes Classifier, Maximum Likelihood |
| 4 | Classification Performance Metrics, Model Selection |
| 5 | Linear Regression |
| 6 | Dimension Reduction |
| 7 | Clustering |
| 8 | Decision Trees |
| 9 | Midterm |
| 10 | Support Vector Machines |
| 11 | Neural Networks |
| 12 | Deep Learning, Auto-encoding, Generative Adversarial Networks |
| 13 | Ensemble Methods: Bagging, Boosting |
| 14 | Project Presentations |
| 15 | Final exam preparation |
| 16 | Final exam |
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 4 | 20 |
| Presentation | 0 | 0 |
| Project | 1 | 10 |
| Seminar | 0 | 0 |
| Quiz | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade success | 60 | |
| Percentage of final exam contributing grade success | 40 | |
| Total | 100 | |
| Course activities | Number | Duration (hours) | Total workload |
|---|---|---|---|
| Course Duration | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, etc.) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 30 | 30 |
| Homework assignment | 4 | 3 | 12 |
| Quiz | 0 | 0 | 0 |
| Midterms (Study Duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total workload | 35 | 106 | 219 |
| Key learning outcomes | Contribution level | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1. | Possesses the theoretical and practical knowledge required in Electrical and Electronics Engineering discipline. | |||||
| 2. | Utilizes his/her theoretical and practical knowledge in the fields of mathematics, science and electrical and electronics engineering towards finding engineering solutions. | |||||
| 3. | Determines and defines a problem in electrical and electronics engineering, then models and solves it by applying the appropriate analytical or numerical methods. | |||||
| 4. | Designs a system under realistic constraints using modern methods and tools. | |||||
| 5. | Designs and performs an experiment, analyzes and interprets the results. | |||||
| 6. | Possesses the necessary qualifications to carry out interdisciplinary work either individually or as a team member. | |||||
| 7. | Accesses information, performs literature search, uses databases and other knowledge sources, follows developments in science and technology. | |||||
| 8. | Performs project planning and time management, plans his/her career development. | |||||
| 9. | Possesses an advanced level of expertise in computer hardware and software, is proficient in using information and communication technologies. | |||||
| 10. | Is competent in oral or written communication; has advanced command of English. | |||||
| 11. | Has an awareness of his/her professional, ethical and social responsibilities. | |||||
| 12. | Has an awareness of the universal impacts and social consequences of engineering solutions and applications; is well-informed about modern-day problems. | |||||
| 13. | Is innovative and inquisitive; has a high level of professional self-esteem. | |||||
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest