Course Information
Course title
 Machine Learning 
Semester
 112-1 
Designated for
 GRADUATE INSTITUTE OF BIOMEDICAL ELECTRONICS AND BIOINFORNATICS  
Instructor
 Tzu-Yu Liu 
Curriculum Number
 EE5184 
Curriculum Identity Number
 921EU2620 
Class
 01 
Credits
 4.0 
Full/Half
Yr.
 Half 
Required/
Elective
 Elective 
Time
 Wednesday 2,3,4,5(9:10~13:10) 
Remarks
 The upper limit of the number of students: 90.
The upper limit of the number of non-majors: 20. 
  
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
 Table of Core Capabilities and Curriculum Planning
Course Syllabus
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Course Description

The machine learning course is a comprehensive program designed to engage in data-driven decision-making. Throughout the course, students will learn to understand the fundamental principles and techniques that underpin modern machine learning. We will explore topics such as supervised and unsupervised learning, Bayesian and non-Bayesian approaches. The curriculum also includes hands-on practical exercises, enabling students to develop a strong foundation in data preprocessing, model selection, and evaluation on real-world applications. By the end of the course, participants emerge with a profound understanding of machine learning algorithms, the ability to implement them, and the confidence to harness the power of data to drive innovation and insights in various domains. 

Course Objective
 - Explain the mathematics behind the Machine Learning models.
- Implement Machine Learning methods and apply them to real applications
- Analyze and critique the numerical results 
Course Requirement
 Language: This course will be offered in English, including the homeworks and exams. However, students can ask questions in the language they feel most comfortable.

Class arrangement: This is a 4-credit course, taking place every Wednesday 9:10 am - 13:10 pm.The first 3 hours (9:10 am - 12:10 pm) will be the lectures. The last hour (12:20-13:10) will be the TA session.
 
Student Workload (expected study time outside of class per week)
 4-5 hours per week of studies and homework after class. 
Office Hours
 Fri. 09:00~10:00 
Designated reading
 NA.  
References
 - James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.
- Zhang, Aston, et al. "Dive into deep learning." arXiv preprint arXiv:2106.11342 (2021).
- Blum, Avrim, John Hopcroft, and Ravindran Kannan. Foundations of data science. Cambridge University Press, 2020.
- Shalev-Shwartz, Shai, and Shai Ben-David. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
- Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022.
- Akkus, Cem, et al. "Multimodal Deep Learning." arXiv preprint arXiv:2301.04856 (2023).
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. 
Grading
  
No.
Item
%
Explanations for the conditions
1. 
 Midterm 
30% 
  
2. 
 Final exam 
30% 
  
3. 
 Homework 
40% 
 The assignments include take-home quizzes implementation on colab. 
 
Adjustment methods for students
  
Teaching methods
 Assisted by recording
Assignment submission methods
Exam methods
Others
 Negotiated by both teachers and students
Progress
Week
Date
Topic
Week 1
 9/06  Introduction and rubric review; What is AI? What is ML?  
Week 2
 9/13  Regression; Cross validation; Hyperparameter optimization  
Week 3
 9/20  Support vector machine (SVM); the kernel trick  
Week 4
 9/27  Decision tree, naive Bayes classifier  
Week 5
 10/04  Ensemble methods, random forest and AdaBoost  
Week 6
 10/11  Dimension reduction, PCA, MDS, tSNE  
Week 7
 10/18  GMM as an example of Bayesian modeling 
Week 8
 10/25  Midterm  
Week 9
 11/01  Multi layer perceptron (MLP)  
Week 10
 11/08  Convolutional neural network (CNN)  
Week 11
 11/15  Autoencoder  
Week 12
 11/22  Variational aucoencoder (VAE)  
Week 13
 11/29  Generative adversarial network (GAN)  
Week 14
 12/06  Transformer  
Week 15
 12/13  Guest lecture  
Week 16
 12/20  Final exam