2147334 ML&DL (1/2023)

1. Midterm Registration: https://forms.gle/6MHRAfn9iDwq3B4e7 

   1. Score (Colab)

   2. Total 95 points

   3. Mean: 88.44

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

3. Shared Drive (all lecture notes are uploaded here): https://drive.google.com/drive/folders/136FIuxa4JNiLZu4vAYd4eQZiqBCZxLKO?usp=sharing 

4. Schedule:

Topics

7-Aug-23

Course Introduction 

21-Aug-23

Hypothesis Representation (VDO Playlist, Colab Link)

1. Introduction to Machine Learning (16:07 mins)

Objectives: Know the brief concept of Supervised learning, Unsupervised learning, and Reinforcement Learning

2. Hypothesis Representation (15:32 mins)

Objective: See some examples of hypothesis presentations in 2D space 

3. Introduction to Sigmoid Function (7:20 mins)

Objective: Know the concept of Sigmoid Function

4. Behavior of Sigmoid Function (15:05 mins)

Objective: Introduction to a parameter tuning 

5. Curve Fitting, Trend Prediction using Sigmoid Function (Python Coding)  (23:24 mins)

Objective: Can see and example of parameter tuning using sigmoid function 

6. Hypothesis Space  (16:09 mins)

Objective: Know the size of hypothesis space

28-Aug-23

Version Space (VDO Playlist)

1. Intro to Version Space (6:14 mins)

2. Data and Hypothesis Representaion (4:41 mins)

3. Explanation of Hypothesis Representation (6:13 mins)

4. Size of Hypothesis Space (8:57 mins)

5. General and Specific Concept from the text book slides (6:03 mins)

6. Find-S Algorithm (6:12 mins)

7. Demonstration of Find-S (6: 31 mins)

8. Search Lattice of Find-S (8:43 mins)

9. Bias of Find-S (6:06 mins)

10. Intro to Version Space (the Candidate Elimination Algorithm( 8:56 mins)

11. An Example of the Version Space Algorithm (15:57 mins)

4-Sep-23

Decision Tree Learning (VDO Playlist)

1. Theory

   1. Introduction to Decision Tree (10:54)

   2. Do You Have a Measurement (6:22)

   3. Introduction to Entropy (14:25)

   4. Finding Entropy (9:55)

   5. Build a Full Tree (8:05)

   6. Colab Link

   7. Recorded Zoom

2. Writing Program 

11-Sep-23

Neural Networks (1) (VDO Playlist)

1. Know the structure of Neuron

2. Know that a Neuron is equal to a linear in 2D space 

3. Can find a weight vector that classify the OR table 

4. Know how to train a perceptron using a spreadsheet 

   1. Training a perceptron (Spreadsheet)

   2. Visualize a perceptron (Colab)

5. Know a sigmoid unit 

6. Know how to train a sigmoid unit

18-Sep-23 

Hypotheses Evaluation (VDO Playlist) 

1. Know components in confusion matrix 

2. Know meaning of precision and recall 

3. Know what are FPR, FNR, TPR, TNR

4. Can write a program which can calculate the confusion matrix of a testing 

5. Can write a program to compare two learning algorithms 

6. Q&A will be held on 25 Sep 23 (16:30 via Zoom)

   1. Please evaluate DTL and KNN on IRIS data

      1. Construct a confusion matrix for both DTL and KNN using train_test_split()

      2. Using 5-fold Cross Validation to compare both algorithms. Please find the significant level also. 

25-Sep-23

Midterm Exam Week 

• We'll have a midterm review class and a review of Evaluating hypothesis at 16:30 via zoom. 

• Recorded Zoom

2-Oct-23

Midterm, Please ignore the schedule in reg.chula.ac.th

9-Oct-23

Neural Networks (2) (VDO Playlist)

1. Can proof how to train a TLN (Spreadsheet)

2. Sigmoid Graph (Colab)

3. Recorded Zoom (Link)

16-Oct-23

Neural Networks (2) 

1. VDO Playlist

   1. Review of Linear Node Training Equation 

   2. An Analysis of Sigmoid Function (Colab)

   3. Finding SIgmoid Training Rule

   4. Training Gaussian Node

   5. MLP Spreadsheet

23-Oct-23

Neural Network (3)

1. Review of Linear and Sigmoid Training

30-Oct-23 

Watch this VDO Playlist (Slides from Tom Mitchel's Text Book)

1. Can design a MLP

2. Can train a BNN

   1. Spreadsheet

   2. Colab Animation

   3. Colab BNN 12 Examples

6-Nov-23

Naive Bayes (VDO Playlist)

1. Naive Bayes Classifier: Bayes Theorem, A Maximum Posterior Hypothesis

Learn Bayes Theorem and Maximum Posterior Hypothesis. When you know some fact, find the hypothesis matches that fact.

2. Naive Bayes: An ATK Example

Learn an example using the "Real World Maximum Posterior Hypothesis". However, you have to think about the case of P(covid19) of the population and how does this probability change if you decide to take the test. If you decide to take the test, it means that your chance to be the covid19 class is much highter than the P(covid19).

3. Naive Bayes Classifier

Learn the concept of Naive Bayes Classifier. Try to understand the example shown in the VDO. 

4. Text Classification Using Naive Bayes

Learn the steps of applying the Naive Bayes Classifier to classify text. 

5. Source Code Explanation

You should be able to apply the source code to attack your real problem.

13-Nov-23

LSTM and CNN

1. CNN Playlist

2. In-class Colab

20-Nov-23

1. LSTM Playlist

After 20-Nov-23

Project Presentation: Put Everything Together (work in a group of 2-3 persons).

1. VDO Presentation

2. Apply your knowledge from our class to attack the problem that you are interested

   1. One technique (Curve Fitting, Decision Tree, Neural Networks, CNN, LSTM, Clustering, Naive Bayes, etc.) 20 Marks

   2. Two or more techniques (Comparing or Combining 2 or more techniques) 25 Marks

3. The problem "must" be your own data (or get raw data from the internet)

   1. Your own data or Get raw data from the internet or Covid19 with some data processing or PM2.5 data 25 Marks

   2. Data (excel, json, csv) from the internet 20 Marks

   3. Data from kaggle.com 15 Marks

4. Analyze results from your technique

   1. Without results discussion (20 Marks)

   2. Can show your understanding of the obtained results (25 Marks)

5. Fine-tune your parameters

   1. Do only one experiment (No parameter tuning) (15 Marks)

   2. Can adjust the parameters to get better results (20 Marks)

   3. Add other techniques to improve the result (25 Marks)

5. Grading

   1. Midterm 30%

   2. Final Project 40%

      1. Please submit your VDO presentation before 23:59, 11 Dec 23.

      2. Please use this form https://forms.gle/QLUjWWk4g43341Xe6 to submit your work

   3. Final 30%

      1. Open Books and Notes 

      2. Calculator is allowed 

      3. No tablet, laptop

      4. Date Time: 8:30-10:30, 4 Dec 23

      5. Place: ENG 3, 420

6. Textbook: 

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