2110743 Machine Learning (2/2025)

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

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

Schedule:

1. 11 Jan 2026: Course Introduction, Intro to Machine Learning (VDO Playlist, Slides from Tom Mitchell Textbook)

2. 18 Jan 2026: Concept Learning (Version Space, VDO Playlist, Slides from Tom Mitchell Textbook, Colab)

3. 25 Jan, 1 Feb 2026: Decision Tree Learning+XGBoosting (VDO Playlist, Slides from Tom Mitchell Textbook)

   • 1. Entropy (Colab)

     2. sklearn (Colab)

4. 8 Feb 2026: Neural Network(VDO Playlist ดูถึง More Area of Perceptrons, Slides from Tom Mitchell Textbook)

   • Random Forest (Colab)

5. 15, 22 Feb 2026: Neural Network(VDO Playlist ดูจนจบ Training a Perceptron, Slides from Tom Mitchell Textbook)

   • Perceptron Training Spreadsheet 

   • Perceptron Training Colab

   • Linear Node Training Spreadsheet

6. 1 Mar 2026: Midterm 

7. 8 Mar 26: Backpropagation Neural Network (VDO Playlist)

   • Colab for confusion matrix 

8. 15 Mar 26: Evaluating Hypothesis (VDO Clips,  Spreadsheet Link, Colab Link [ROC])

9. 22 Mar 26: SVM (VDO Clips, Colab, Book Chapter )

10. 5 Apr 26 CNN (VDO Clips) 

    • Pretrained CNN: colab

    • Transfer Learning: colab 

    • MNIST Keras: colab

    • LSTM (VDO Clips) 

11. 19 Apr 26: Naive Bayes Learning (VDO Clips), Colab Link of LSTM for Text Classification)

12. 26 Apr 26 Clustering (VDO Clips)

13. [Tentative] Reinforcement Learning (VDO Clips)

14. 10 May 26 Final Exam (VDO Clip)

15. Project Presentation (Offline VDO)

    • Submitted via a form

    • 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.