CO6062:
Machine Learning Algorithms, Fall 2025
Lecturer:
Prof. Dah-Chung Chang, E-mail: dcchang@ce.ncu.edu.tw
Office: E1-311, TEL: 03-4227151 ext. 35511
Lecture Time:
Tuesday 1:00pm-4:00pm
TA Information:
E1-306, TEL:
03-4227151 ext 35534
Pre-requisite:
Programming,
Linear Algebra, Calculus, Probability Theory, (Adaptive Filtering Theory)
Course Goal:
You can learn main knowledge of machine learning
and deep learning algorithms and their programming skills with MATLAB and
Python/Pytorch on Google’s TensorFlow such that you can apply them to the
fields you are studying or will study. Please group your team for the final
project at most two persons.
Course Outline:
Part A: Supervised Learning
Least Squares and
Linear Regression Classifications
Linear Discriminant Analysis (LDA)
Bayesian Classification
Logistic Regression (LR)
Decision Tree
Support Vector Machine (SVM)
Kernel Method
MATLAB Exercises
Part B: Unsupervised Learning
Clustering
K-Means
Principal Component
Analysis (PCA)
butterfly.gif pca
Singular Value
Decomposition (SVD)
Independent
Component Analysis (ICA)
MATLAB Exercises
Part C: Deep Learning
Multilayer
Perceptron (MLP)
Deep Neural
Networks (DNN)
Convolutional
Neural Networks (CNN)
Recurrent Neural
Networks (RNN)
Long Short-Term
Memory (LSTM)
Python/Pytorch
Tutorial Lab./Exercises
Part D: Advanced AI Topics
Generative
Adversarial Network
Reinforcement
Learning
Autoencoder
Ensemble
Learning
Homeworks Assignment:
* Note:
Each team needs to email your homeworks along with codes for
score evaluation. If no figures and codes are attached to your reports, we
cannot judge the validity.
Notice: Upload will be automatically closed after
2 days over the deadline.
|
Items |
Deadline |
Subject |
Upload
web site |
|
HW#1 |
2025/10/28 |
||
|
HW#2 |
2025/11/11 |
ICA for Blind Source
Separation File: X.zip |
|
|
HW#3 |
Contacting TA |
TA_Lab1: SVM with Python (See Lecture notes) TA ML Python Coding Tutorial |
|
|
HW#4 |
Contacting TA |
TA_Lab2: CNN and RNN with Python (See Lecture notes) TA DL Python Coding Tutorial |
|
|
HW#5 |
2025/12/3 |
||
|
Team Report |
(Team #1-#5) 2025/12/2 (Team #6 - ) |
PPT report and discussion for Team Project plan and AI technology
(30
mins
for each team) |
|
|
Final Project |
2025/12/15 |
Final Project Report Delivered |
|
|
Final Project Codes: ZIP Upload |
2025/12/15 |
Final Project Codes ZIP file Delivered |
|
Final Project:
Final Project Deadline: 2025/12/15
Final Project (about your project’s AI
Technology) Introduction (max. 30 mins/team): 2025/11/25 (Team #1-#5), 2025/12/2 (Team #6 - )
-
You
SHALL list the homework/project title and member names/IDs (one person for all homeworks) on the
first page of your reports, and upload your homeworks, reports, and final
project documents along with MATLAB/Python/Pytorch
codes in the .doc format (Word) and the Presentation Report (PPT) to the upload
web sites.
Notice
that in your word .doc report, the detailed materials about your problem,
system model, algorithms, simulation results, references, and codes SHALL be
described and explained!
The uploading date should be
not late than the deadline.
Course Materials
Textbook:
Lecture
Notes
References:
See them in Lecture Notes
Grading
1.
Policy: Homeworks*75%+Final Project*20%+Team Project Discussion*5%
2.
Term Grading Report
