Syllabus ( CSE 552 )
|
|
Basic information
|
|
| Course title: |
Machine Learning |
| Course code: |
CSE 552 |
| Lecturer: |
Assoc. Prof. Dr. Yakup GENÇ
|
| ECTS credits: |
7.5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
1/2, Fall and Spring |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Area Elective
|
| Language of instruction: |
Turkish
|
| Mode of delivery: |
Face to face , Lab work
|
| Pre- and co-requisites: |
None |
| Professional practice: |
No |
| Purpose of the course: |
The main objective of this course is to teach students the state-of-the-art machine learning techniques, enable them to apply these techniques to measured data and bring them to the level of understanding the scientific publications. |
|
|
|
Learning outcomes
|
|
Upon successful completion of this course, students will be able to:
-
Apply the essential principles of machine learning concept to real-life data.
Contribution to Program Outcomes
-
Define and manipulate advanced concepts of Computer Engineering
-
Use advanced knowledge of mathematics, science, and engineering
-
Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
Method of assessment
-
Homework assignment
-
Identify the differences between machine learning methods and gain the skills of selecting an appropriate method for a given data.
Contribution to Program Outcomes
-
Define and manipulate advanced concepts of Computer Engineering
-
Use advanced knowledge of mathematics, science, and engineering
Method of assessment
-
Written exam
-
Homework assignment
-
Analyze the performance and the results of a machine learning method in terms of error complexity.
Contribution to Program Outcomes
-
Define and manipulate advanced concepts of Computer Engineering
-
Use advanced knowledge of mathematics, science, and engineering
Method of assessment
-
Written exam
|
|
|
Contents
|
|
| Week 1: |
Introduction |
| Week 2: |
Supervised learning |
| Week 3: |
Bayesian learning |
| Week 4: |
Model selection |
| Week 5: |
neural network |
| Week 6: |
nearest neighbor |
| Week 7: |
Naïve Bayes |
| Week 8: |
Midterm exam |
| Week 9: |
Support vector machines |
| Week 10: |
Decision trees |
| Week 11: |
Experimental design and evaluation |
| Week 12: |
Computational learning theory |
| Week 13: |
Ensemble methods |
| Week 14: |
Unsupervised learning |
| Week 15*: |
- |
| Week 16*: |
Final exam |
| Textbooks and materials: |
Introduction to Machine Learning (Adaptive Computation and Machine Learning) by Ethem Alpaydin, 1st Edition, The MIT Press, October 2004.
|
| Recommended readings: |
- T. M. Mitchell. Machine Learning. 1997.
- Chris Bishop, Pattern Recognition and Machine Learning, Springer 2006. |
|
|
* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
|
|
|
|
|
Assessment
|
|
|
| Method of assessment |
Week number |
Weight (%) |
|
| Mid-terms: |
8 |
25 |
| Other in-term studies: |
0 |
0 |
| Project: |
14 |
25 |
| Homework: |
3,6,9,12 |
25 |
| Quiz: |
0 |
0 |
| Final exam: |
16 |
25 |
| |
Total weight: |
(%) |
|
|
|
|
Workload
|
|
|
| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
|
| Courses (Face-to-face teaching): |
3 |
14 |
|
| Own studies outside class: |
3 |
14 |
|
| Practice, Recitation: |
0 |
0 |
|
| Homework: |
15 |
4 |
|
| Term project: |
0 |
0 |
|
| Term project presentation: |
0 |
0 |
|
| Quiz: |
0 |
0 |
|
| Own study for mid-term exam: |
15 |
1 |
|
| Mid-term: |
1 |
1 |
|
| Personal studies for final exam: |
20 |
1 |
|
| Final exam: |
2 |
1 |
|
| |
|
Total workload: |
|
| |
|
Total ECTS credits: |
* |
|
|
* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
|
|
|
-->
