| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Employ known algorithms to solve given problems
- Use software tools
- Develop critical thinking skills
- Propose and design new systems, by extending existing algorithms or exploring novel approaches, to meet the given requirements
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | This course provides students with the ability to design and develop systems to obtain the required information by detecting complex patterns in existing data with different algorithms or combinations of algorithms. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction, course structure, overview of machine learning and deep learning. |
|
2nd Week |
Optimization. |
|
3rd Week |
Feedforward networks and training. |
|
4th Week |
Feedforward networks and training; |
|
5th Week |
Convolutional neural networks. |
|
6th Week |
Convolutional neural networks. |
|
7th Week |
Deep learning for spatial localization. |
|
8th Week |
Midterm |
|
9th Week |
Recurrent neural networks. |
|
10th Week |
Recurrent neural networks. |
|
11th Week |
Deep generative models. |
|
12th Week |
Deep generative models. |
|
13th Week |
Deep reinforcement learning. |
|
14th Week |
Deep reinforcement learning. Project presentations. |
|
| Recommended or Required Reading | Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, 2016, MIT Press |
|---|
| Planned Learning Activities and Teaching Methods |
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
3 |
42 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
25 |
25 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Project |
1 |
12 |
12 |
| Total Work Load | 150 |
| Total Work Load / 30 (h) | 5 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
