Zoom Link: https://chula.zoom.us/j/7474759219
Shared Drive (all lecture notes are uploaded here): https://drive.google.com/drive/folders/136FIuxa4JNiLZu4vAYd4eQZiqBCZxLKO?usp=sharing
Schedule:
Topics
Course Introduction
(18 Aug 2025) Hypothesis Representation (VDO Playlist)
Introduction to Machine Learning (16:07 mins)
Objectives: Know the brief concept of Supervised learning, Unsupervised learning, and Reinforcement Learning
Hypothesis Representation (15:32 mins)
Objective: See some examples of hypothesis presentations in 2D space
Introduction to Sigmoid Function (7:20 mins)
Objective: Know the concept of Sigmoid Function
Behavior of Sigmoid Function (15:05 mins)
Objective: Introduction to a parameter tuning
Curve Fitting, Trend Prediction using Sigmoid Function (Python Coding) (23:24 mins)
Objective: Can see and example of parameter tuning using sigmoid function
Hypothesis Space (16:09 mins)
Objective: Know the size of hypothesis space
Colab Link
25-Aug-25
Version Space (VDO Playlist)
Intro to Version Space (6:14 mins)
Data and Hypothesis Representaion (4:41 mins)
Explanation of Hypothesis Representation (6:13 mins)
Size of Hypothesis Space (8:57 mins)
General and Specific Concept from the text book slides (6:03 mins)
Find-S Algorithm (6:12 mins)
Demonstration of Find-S (6: 31 mins)
Search Lattice of Find-S (8:43 mins)
Bias of Find-S (6:06 mins)
Intro to Version Space (the Candidate Elimination Algorithm( 8:56 mins)
An Example of the Version Space Algorithm (15:57 mins)
1-Sep-25
Decision Tree Learning (VDO Playlist)
Theory
Introduction to Decision Tree (10:54)
Do You Have a Measurement (6:22)
Introduction to Entropy (14:25)
Finding Entropy (9:55)
Build a Full Tree (8:05)
Writing Program
8-Sep-25
Neural Networks (1) (VDO Playlist)
Know the structure of Neuron
Know that a Neuron is equal to a linear in 2D space
Can find a weight vector that classify the OR table
Know how to train a perceptron using a spreadsheet
Training a perceptron (Spreadsheet, 2025 Version)
Visualize a perceptron (Colab)
Know a sigmoid unit
Know how to train a sigmoid unit
15-Sep-25
Hypotheses Evaluation (VDO Playlist)
Know components in confusion matrix
Know meaning of precision and recall
Know what are FPR, FNR, TPR, TNR
Can write a program which can calculate the confusion matrix of a testing
Can write a program to compare two learning algorithms
Q&A will be held on 25 Sep 23 (16:30 via Zoom)
Please evaluate DTL and KNN on IRIS data
Construct a confusion matrix for both DTL and KNN using train_test_split()
Using 5-fold Cross Validation to compare both algorithms. Please find the significant level also.
22-Sep-25
Midterm Exam Week
6-Oct-25
Neural Networks (2) (VDO Playlist)
Can proof how to train a TLN (Spreadsheet)
Sigmoid Graph (Colab)
Recorded Zoom (Link)
20-Oct-25
Neural Networks (2)
Review of Linear Node Training Equation
An Analysis of Sigmoid Function (Colab)
Finding SIgmoid Training Rule
Training Gaussian Node
MLP Spreadsheet
27-Oct-25
Neural Network (3)
Review of Linear and Sigmoid Training
3-Nov-25
Watch this VDO Playlist (Slides from Tom Mitchel's Text Book)
Can design a MLP
Can train a BNN
10-Nov-25
Naive Bayes (VDO Playlist)
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.
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).
Naive Bayes Classifier
Learn the concept of Naive Bayes Classifier. Try to understand the example shown in the VDO.
Text Classification Using Naive Bayes
Learn the steps of applying the Naive Bayes Classifier to classify text.
Source Code Explanation
You should be able to apply the source code to attack your real problem.
17-Nov-25
LSTM and CNN
24-Nov-25
After 20-Nov-23
Project Presentation: Put Everything Together (work in a group of 2-3 persons).
VDO Presentation
Apply your knowledge from our class to attack the problem that you are interested
One technique (Curve Fitting, Decision Tree, Neural Networks, CNN, LSTM, Clustering, Naive Bayes, etc.) 20 Marks
Two or more techniques (Comparing or Combining 2 or more techniques) 25 Marks
The problem "must" be your own data (or get raw data from the internet)
Your own data or Get raw data from the internet or Covid19 with some data processing or PM2.5 data 25 Marks
Data (excel, json, csv) from the internet 20 Marks
Data from kaggle.com 15 Marks
Analyze results from your technique
Without results discussion (20 Marks)
Can show your understanding of the obtained results (25 Marks)
Fine-tune your parameters
Do only one experiment (No parameter tuning) (15 Marks)
Can adjust the parameters to get better results (20 Marks)
Add other techniques to improve the result (25 Marks)
Grading
Midterm 30%
Final Project 40%
Please submit your VDO presentation before 23:59, 11 Dec 23.
Please use this form https://forms.gle/QLUjWWk4g43341Xe6 to submit your work
Final 30%
Open Books and Notes
Calculator is allowed
No tablet, laptop
Textbook: