2110743 Machine Learning (2/2024)

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

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

3. คะแนนสอบกลางภาค

1. How to get the midterm score:

   1. add line account @288sdzje 

2. type "register xxxxxxxxxx" where xxxxxxxxxx is your student ID (you can skip this if you've already registered with this chat)

3. type "score 2110743"

2. Stat: Mean 92.77, SD 23.37, เต็ม 120

4. Schedule:

   1. 12 Jan 2025: Course Introduction, Intro to Machine Learning (VDO Playlist, Slides from Tom Mitchell Textbook)

   2. 19 Jan 2025: Concept Learning (Version Space, VDO Playlist, Slides from Tom Mitchell Textbook, Colab)

   3. 26 Jan, 2 Feb 2025,: Decision Tree Learning+XGBoosting (VDO Playlist, Slides from Tom Mitchell Textbook)

      • 1. Entropy (Colab)

        2. sklearn (Colab)

   4. 9 Feb 2025: Neural Network(VDO Playlist ดูถึง More Area of Perceptrons, Slides from Tom Mitchell Textbook)

      • Random Forest (Colab)

   5. 16, 23 Feb 2025: Neural Network(VDO Playlist ดูจนจบ Training a Perceptron, Slides from Tom Mitchell Textbook)

      • Perceptron Training Spreadsheet 

      • Perceptron Training Colab

      • Linear Node Training Spreadsheet

      • Linear Node Weather Forecast Colab

   6. 2 Mar 2025: Perceptron Training + Midterm Review

      • Colab

   7. 9 Mar 2025: Midterm 

   8. 16, 23 Mar 25: Backpropagation Neural Network (VDO Playlist)+Evaluating Hypothesis (VDO Clips, Colab Link)

      • Colab for confusion matrix 

   9. 30 Mar 25: SVM (VDO Clips, Colab)

   10. 6 Apr 25 CNN (VDO Clips) 

       • Pretrained CNN: colab

       • Transfer Learning: colab 

       • MNIST Keras: colab

       • LSTM (VDO Clips) 

   11. 20 Apr 25: Naive Bayes Learning (VDO Clips), Colab Link of LSTM for Text Classification)

   12. 27 Apr 25 Clustering (VDO Clips)

   13. 4 May 25 Reinforcement Learning (VDO Clips)

   14. 11 May 25 Final Exam (VDO Clip)

   15. Project Presentation (Offline VDO)

       • Submitted via a form https://forms.gle/9zac15mXPq3Sr4mF8 (before 20 May 2025)

       • 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 

5. Grading

   1. Midterm 30% 

   2. Final 40% 

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

6. Text book: 

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

   2. Text book slides are here.