Neural Network (2/2024)

1. ประกาศ

***  วันที่ 28 เมษายน 2568 สอบปลายภาค เวลา 13:00-15:00 ห้อง 418, 419 ตึก 3  ***

***  ข้อสอบเก่าอยู่ใน Shared Folder ***

2. Zoom Link: https://chula.zoom.us/j/7474759219

3. Shared Drive: https://drive.google.com/drive/folders/158hhw0J95e5eHnrhN_rgwmt9jr9_7X3Y?usp=sharing 

4. Course Schedule:

   1. 6 Jan 25: Course Introduction (Brain System, Artificial Neuron) 

   2. 13 Jan 25: Artificial Neuron Architecture (VDO Playlist) 

   3. 20, 27 Jan 25: TLN Architecture (VDO Playlist, In-class Colab)

      1. Please focus on the following VDOs

         1. Dichotomies and Capacity of a Perceptron 

         2. Number of Regions Separated by Perceptrons  

   4. 3 Feb 25, TLN Training+Supervised Learning  (1) (VDO Playlist)

      1. Spreadsheet (Link)

      2. Colab Error Surface (Link)

   5. 10 Feb 25: TLN Training+Supervised Learning (2) (VDO Playlist)

      1. Training Gaussian Function (Colab)

   6. 17 Feb 25: TLN Training+Supervised Learning (3) (VDO Playlist, Training Spreadsheet, Weiner Solution Colab) 

   7. 24 Feb 25: Midterm Review

   8. 3 Mar 25: Midterm (ไม่มีการเรียนการสอน) 

   9. 17 Mar 25: Midterm

   10. 24 Mar 24: Backpropagation Neural Network  (VDO Clips) 

       1. This one is not in the VDO. An analysis of a sigmoid unit https://colab.research.google.com/drive/1vdZjNpBia28fsHB5XoFJhs_eQnOpcTeM?usp=sharing 

       2. Tom Mitchell Slide: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/mlbook/ch4.pdf 

   11. 31 Mar 25 Quiz BNN+

   12. 31 Mar 25 Support Vector Machine  (VDO Clips, Colab)

   13. 7 Apr 25 Self Organizing Feature Map (VDO Clips)

   14. 21 Apr 25 Convolution Neural Network (VDO Clips), LSTM (VDO Clips)

   15. 28 Apr 25 Final Exam

5. Grading System

   1. Midterm 35%

   2. Final 45%

      1. Closed book, Calculators are allowed

      2. Tablet, and Computer are "not"  allowed.

   3. Project 20%

      1. 2-3 Members 

      2. Apply techniques in the class to a new data set

      3. Submit in a recorded VDO not longer than 10 minutes 

         1. Choose one of your interesting chapter 

         2. Demonstrate your understanding of that chapter 

         3. Slightly change the code and demonstrate the change  

      4. Don't forget to add your group member in the submitted form 

         1. The deadline is 15 May 2025

         2. Submit via this form (https://forms.gle/QQ2DCqBxD1D88dHc8) 

6. References:

   1. Kumar, S. (2013). Neural Networks: A Classroom Approach. India: McGraw-Hill.

   2. Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach. Hoboken, NJ: Pearson.