| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 7 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Understand what an artificial neural network (ANN) is.
- Understand why ANN's are crucial for psychology and neuroscience.
- Gain knowledge about how ANN's work.
- Gain knowledge about different types of ANN's.
- Find opportunity to simulate a learning system with an easy neural net simulator.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course introduces students to artificial neural networks and the use of artificial neural networks in the modeling of human behavior and cognition. Topics covered in the course include: the structure and components of artificial neural networks, connection geometry, network parameters, network types, threshold functions, learning paradigms and algorithms, memory properties, generalization and computational power of artificial neural networks. The course also examines the perceptron model, connectionism and connectionist models, and parallel distributed processing. Drawing on sample network models of visual perception, learning, speech perception, language acquisition, and learning, the course aims to inform students of the use of artificial neural networks in psychological research. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction |
|
2nd Week |
Neurons |
|
3rd Week |
Artificial Neurons and Artificial Neural Networks |
|
4th Week |
Lateral Inhibition and Sensory Processing |
|
5th Week |
Types and History of ANN |
|
6th Week |
Artificial Network Algorithms |
|
7th Week |
Learning Algorithms |
|
8th Week |
Mid-term Exam |
|
9th Week |
T-learn Neural Network Simulation Program |
|
10th Week |
Constructing a simple ANN with T-learn |
|
11th Week |
Criticisms of ANN |
|
12th Week |
Representation of Information |
|
13th Week |
ANN's and Neuroscience |
|
14th Week |
Hybrid Models |
|
15th Week |
Hybrid Models |
|
| Recommended or Required Reading | Anderson, J.A., (1995). An Introduction to Neural Networks. MIT Press.
Bar-Yam, Y. (1997). Dynamics of complex systems. Addison-Wesley.
Rumelhart, D., & J. McClelland. (1986). Parallel distributed processing. MIT Press, Cambridge, Mass.
|
|---|
| Planned Learning Activities and Teaching Methods |
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
40 |
| Total(%) |
|
70 |
| Contribution of In-term Studies to Overall Grade(%) |
|
70 |
| Contribution of Final Examination to Overall Grade(%) |
|
30 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
3 |
3 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
8 |
112 |
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
15 |
3 |
45 |
| Preparation for Midterm exam |
1 |
15 |
15 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
3 |
3 |
| Homework |
|
|
|
| Project |
1 |
10 |
10 |
| Total Work Load | 208 |
| Total Work Load / 30 (h) | 6.93 |
| ECTS Credit of the Course | 7 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
