ARTIFICIAL NEURAL NETWORK (2/2 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Explain the concept of neural network; Analyze several cases using the concept of neural network; Solve problems using neural network models; Create applications using the concept of neural network.

Topics:

1. Introduction to Python
2. Perceptron and LMS with Python
3. Backpropagation Neural Network with Tensorflow
4. BPNN Parameter Tuning
5. Quiz 1
6. RNN Parameter Tuning
7. Self-Organizing Map
8. Quiz 2
9. Principal Component Analysis
10. CNN
11. Project
12. Introduction to Artificial Neural Network
13. Single Layer Perceptron
14. Multilayer Feedforward Neural Network 1
15. Multilayer Feedforward Neural Network 2
16. Self Organizing Map (SOM) 1
17. Self Organizing Map (SOM) 2
18. Principal Component Analysis (PCA)
19. Convolution Neural Network (CNN) 1
20. Convolution Neural Network (CNN) 2
21. Review
22. Performance Evaluation
23. Recurrent Neural Network 1
24. Recurrent Neural Network 2