2110743 Machine Learning (2/2023)

Zoom link:  https://chula.zoom.us/j/7474759219 

Shared Folder: https://drive.google.com/drive/folders/1bUEF_lbuAxB1KA1SgIXGmgHbVaVWVpEy?usp=sharing 

Midterm Score: https://ssukree.pythonanywhere.com/get_data?id=your_id&course=2023_2_2110743

1. Mean: 81.96

2. Stdeva: 27.33

Schedule:

1. 14 Jan 2024: Course Introduction, Intro to Machine Learning (VDO Playlist, Slides from Tom Mitchell Textbook)

2. 21 Jan 2024: Concept Learning (Version Space, VDO Playlist, Slides from Tom Mitchell Textbook, Colab)

3. 28 Jan, 4 Feb 2024,: Decision Tree Learning+XGBoosting (VDO Playlist, Slides from Tom Mitchell Textbook)

   • 4 Feb 2024 

     1. Entropy (Colab)

     2. sklearn (Colab)

4. 11 Feb 2024: Neural Network(VDO Playlist ดูถึง More Area of Perceptrons, Slides from Tom Mitchell Textbook)

   • Random Forest (Colab)

5. 18, 25 Feb 2024: Neural Network(VDO Playlist ดูจนจบ Training a Perceptron, Slides from Tom Mitchell Textbook)

   • In class colab (Link)

   • ** 25 Feb 24 **

     1. ไม่มี Onsite นะครับ 

     2. ดูเรื่องการ Train, TLN, Linear และ Sigmoid ครับ

     3. คลาส Zoom เพื่ออธิบายเนื้อหา เริ่มตอน 9:00 น. 

6. 3 Mar 2024: Perceptron Training + Midterm Review

7. 10 Mar 2024: Midterm 

8. 17 Mar 24: Backpropagation Neural Network (VDO Playlist)+Evaluating Hypothesis (VDO Clips, Colab Link)

   • 17 Mar 24 Training Spreadsheet

   • 24 Mar 24 Colab

9. 31 Mar 24: SVM (VDO Clips, Colab)

10. 7 Apr 24 CNN (VDO Clips) 

    • Pretrained CNN: colab

    • Transfer Learning: colab 

    • MNIST Keras: colab

11. 21 Apr 24: LSTM (VDO Clips) + Naive Bayes Learning (VDO Clips), Colab Link of LSTM for Text Classification)

12. 28 Apr 24 Clustering (VDO Clips)

13. Not included in the final exam

    • Reinforcement Learning (VDO Clips)

14. Project Presentation (Offline VDO)

    • Submitted before 19 May 24 via this link https://forms.gle/rNaS17hLJLj9KF8c8 

      1. For the ones who have already submitted via my email before 14 May 24. You're graded and not necessary to submit via the form again. 

    • 2-3 members/group 

    • Choose from 1: Supervised, 2: Unsupervised 

      1. Supervised

         1. Choose 1 dataset 

         2. Apply 2-3 algorithms

         3. Fine tune model to be the best one 

      2. Unsupervised 

         1. Choose 1 dataset 

         2. Try to cluster data 

         3. Explain the result 

Grading

1. Midterm 30% 

2. Final 40% 

3. Project (2-3 Students/group) 30%

Text book: 

1. Tom Mitchell, "Machine Learning", McGraw Hill, 1997.

2. Text book slides are here.