Neural Network (2/2023)

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

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

3. คะแนนกลางภาค (ยังไม่รวม Quiz BNN) 

   1. แก้ XXXXXXXXXX เป็นเลขประจำตัวนิสิต
      ssukree.pythonanywhere.com/get_data?course=2023_2_2110596&id=XXXXXXXXXX 

4. Course Schedule:

   1. 8 Jan 24: Course Introduction (Brain System, Artificial Neuron) 

   2. 15 Jan 24: Artificial Neuron Architecture (VDO Playlist) 

   3. 22, 29 Jan 24: TLN Architecture (VDO Playlist, In-class Colab)

   4. 5 Feb 24, TLN Training+Supervised Learning  (1) (VDO Playlist)

      1. Spreadsheet (Link)

      2. Colab Error Surface (Link)

   5. 12 Feb 24: TLN Training+Supervised Learning (2) (VDO Playlist)

      1. Training Gaussian Function (Colab)

   6. 19 Feb 24: TLN Training+Supervised Learning (3) (VDO Playlist, Training Spreadsheet, Weiner Solution Colab) 

   7. 26 Feb 24: หยุดมาฆบูชา

   8. 4 Mar 24: Midterm (ไม่มีการเรียนการสอน) 

   9. 11 Mar 24: Midterm (Confirmed)

      1. Review Note: อยู่ใน Shared Folder เป็น Note ของวันที่ 19 กุมภาพันธ์ 

   10. 18 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 

   11. 25 Mar 24 Quiz BNN+

   12. 1 Apr 24 Support Vector Machine  (VDO Clips, Colab)

วันนี้ไม่มี onsite นะครับ ดูวิดีโออย่างเดียว SVM (เรื่องจริงนะครับ ไม่ใช่ April fool day)

4. 13. 8 Apr 24 Self Organizing Feature Map (VDO Clips)

   14. 8 Apr 24 Convolution Neural Network (VDO Clips)

   15. 15 Apr 24 LSTM (VDO Clips)

   16. 21 Apr 24  Class Review

   17. 29 Apr 24 Final Exam (13:00-15:00, Room 415, ENG Bldg 3)

5. Grading System

   1. Midterm 35%

   2. Final 45%

      1. Open book, Calculators, 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. Where can you get the data?

         2. Data preprocessing 

         3. Example of data 

         4. How to apply NN to attck your problem 

         5. What's the result 

         6. How can you adjust (fine tune the model)?

         7. How does it improve from the original version?

      4. Submit via this link https://forms.gle/rNaS17hLJLj9KF8c8 before 13 May 24 

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.