ARTIFICIAL NEURAL NETWORKS

AIM

The aim of the course is to evaluate the use of the computational models of the neurons in machine learning and the modeling of the components of the nervous system.

CONTENT

This course contains; The Nervous System: Microscopic View,The Nervous System: Macroscopic View,Machine Learning,Perceptron,Multilayer Perceptron,Supervised Learning,Backpropogation Algorithm,Online Learning,Batch Learning,Overfitting,Neural Networks for Pattern Classification,Neural Networks in Regression,Neuromodulation,Reinforcement Learning.

1. 1

   Designs single layer perceptron.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Problem Baded Learning Model, Experiential Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

2. 2

   Implements the online learning algorithm.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Problem Baded Learning Model, Experiential Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

3. 3

   Develops classifiers using multilayer perceptrons.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Problem Baded Learning Model, Experiential Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

4. 4

   Designs multilayer perceptron for regression.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Problem Baded Learning Model, Experiential Learning, Flipped Classroom Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

WEEKLY PLAN

1. WEEK 1

   The Nervous System: Microscopic View

2. WEEK 2

   The Nervous System: Macroscopic View

3. WEEK 3

   Machine Learning

4. WEEK 4

   Perceptron

5. WEEK 5

   Multilayer Perceptron

6. WEEK 6

   Supervised Learning

7. WEEK 7

   Backpropogation Algorithm

8. WEEK 8

   Online Learning

9. WEEK 9

   Batch Learning

10. WEEK 10

    Overfitting

11. WEEK 11

    Neural Networks for Pattern Classification

12. WEEK 12

    Neural Networks in Regression

13. WEEK 13

    Neuromodulation

14. WEEK 14

    Reinforcement Learning

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• Alpaydin, E., (2010) Introduction to machine learning, MIT Press,Cambridge. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., Hudspeth, A. J. , (2012) Principles of neural science, McGraw-Hill, New York.
• Lytton, W. W., (2002) From computer to brain : foundations of computational neuroscience, Springer, New York. Dayan, P., Abbott, L. F., (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, MIT Press, Cambridge. Izhikevich, E.M., (2007) Dynamical systems in neuroscience: The geometry of excitability and bursting, MIT Press, Cambridge.

TEACHING STAFF

• Assist.Prof. Mehmet KOCATÜRKCOORDINATOR
• Assist.Prof. Mehmet KOCATÜRK