2147334 Machine Learning or Deep Learning

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

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

3. Score Announcement: https://colab.research.google.com/drive/1kTekrSvVAiAPIqHA7-9CT6rKuiIv_wpp?usp=sharing

4. Schedule:

Topics

8-Aug-22

Course Introduction

15-Aug-22

Hypothesis Representation (VDO Playlist, Lecture Note, 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

22-Aug-22, 29-Aug-22

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 Lattic 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)

12. Live Session Starts from 15:30

5-Sep-22

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)

2. Writing Program

12-Sep-21 (Pre Recorded Only, no onsite and zoom class.) All contents will be reviewed on 19 September 2022.

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 Live Session Starts from 15:30 (Recorded 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.

19-Sep-22

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

5. Know a sigmoid unit

6. Know how to train a sigmoid unit

26-Sep-22

Neural Networks (2) (VDO Playlist, Recorded Zoom)

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

3-Oct-22

Midterm Exam Week (No Class)

10-Oct-22

Midterm (at 304/2 Eng 2)

17-Oct-22

Neural Networks (2)

1. VDO Playlist

   1. The first four VDOs are review of training a TLN

   2. Watch the

      1. What's a Linear Node?

      2. Training a Linear Node

      3. Error Surface of a Linear Node

      4. How to Train a Linear Node

2. VDO Playlist

   1. Review of Linear Node Training Equation

   2. Sigmoid Function and Its Derivative

   3. Finding SIgmoid Training Rule

   4. Leave the Gaussian for the next two weeks

31-Oct-22

Neural Network (3)

1. Review of Linear and Sigmoid Training

7-Nov-22 (Fully Online: Preview in Live Session at 14:000)

Watch the Gaussian Part of the VDO Playlist

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

1. Can design a MLP

2. Can train a BNN

14-Nov-22

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.

21-Nov-22

LSTM and CNN

1. CNN Playlist

2. LSTM Playlist

3. Recorded Zoom (Last class)

After 28-Nov-22

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

1. VDO Presentation

   1. 1. 1. Please submit here before 20 December 2022.

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 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%

   3. Final 30%

      1. Open Books and Notes

      2. Calculator is allowed

      3. No tablet, laptop

6. Textbook:

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