Neural Network (2/2025)

** ประกาศคะแนนกลางภาคทาง MCV แล้วนะครับ วันที่ 27 มีนาคม 2569  **

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

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

3. Course Schedule:

   1. 5 Jan 26: Course Introduction (Brain System, Artificial Neuron) 

   2. 12 Jan 26: Artificial Neuron Architecture (VDO Playlist) 

   3. 19, 26 Jan 26: TLN Architecture (VDO Playlist, In-class Colab)

      1. 19 Jan 26 In-class Colab (Link)

      2. Please focus on the following VDOs

         1. Dichotomies and Capacity of a Perceptron 

         2. Number of Regions Separated by Perceptrons  

   4. 9 Feb 26, TLN Training+Supervised Learning  (1) (VDO Playlist)

      1. Spreadsheet (Link)

      2. Colab Error Surface (Link)

   5. 9 Feb 26: TLN Training+Supervised Learning (2) (VDO Playlist)

      1. Training Gaussian Function (Colab)

   6. 16 Feb 26: TLN Training+Supervised Learning (3) (VDO Playlist, Training Spreadsheet, Weiner Solution Colab) 

   7. 23 Feb 26:Midterm (ไม่มีการเรียนการสอน มี Review ตอน 12:00) 

   8. 2 Mar 26: Midterm สอบเวลา 13:00-15:00
      ประกาศห้องสอบ ตึก 3 ทั้งสามกลุ่ม
      ห้อง 306: 6430376521-6631308221
      ห้อง 418: 6631313321-6633032321
      ห้อง 420: 6633034621-6872081621

   9. 9 Mar 26: 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 

   10. 16 Mar 26 Support Vector Machine  (VDO Clips, Colab, Book Chapter)

   11. 23 Mar 26 Self Organizing Feature Map (VDO Clips)

   12. 30 Mar 26 Convolution Neural Network (VDO Clips), LSTM (VDO Clips)

   13. 20 Apr 26 Transformer

4. 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  

5. 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.