Courses
Teaching Philosophy
I believe in a hands-on, project-based approach to teaching computer science that prepares students for real-world challenges while fostering critical thinking and innovation.
Spring 2025 Courses
Machine Learning
College 3rd year
Undergraduate course covering advanced machine learning algorithms and deep learning theory.
Seminar
PhD Students
Students took this course for 3 years. We extensively explore modern AI techniques via intensive discuss.
Research Methods
Master Degree Students
Graduate seminar on research methodologies, paper writing, and academic practices in computer science.
Office Hours (Spring 2025)
- Monday 14:00 - 16:30
- Wednesday 10:00 - 12:00
- Friday By Appointment
Location: 工學大樓 E658
Email: shchen@mail.tku.edu.tw
Note: Please email at least 24 hours in advance to schedule appointments outside regular office hours.
Teaching Assistants (Spring 2025)
Hsin-An Chen
Machine Learning
Office Hours: Tue 10:00-12:00Teaching History
Machine Learning Applications
CS4021
Advanced course on practical applications of machine learning in various domains including healthcare and computer vision.
Introduction to Programming
CS1001
Introductory programming course using Python, focusing on problem-solving and computational thinking.
AI and Deep Learning
CS4023
Graduate course exploring modern AI techniques with emphasis on deep neural networks.
Database Systems
CS3022
Comprehensive course on database design, implementation, and optimization with hands-on SQL projects.
Data Mining
CS5021
Graduate course on data mining techniques and their applications to large-scale datasets.
Various Courses
Including Computer Networks, Software Engineering, Mobile App Development, and more.
Fall 2022
- Computer Networks (CS3015)
- Advanced Machine Learning (CS5003)
Spring 2022
- Operating Systems (CS3017)
- Data Structures and Algorithms (CS2011)
Fall 2021
- Mobile Application Development (CS4015)
- Software Engineering (CS3012)
Graduate Courses
Course Description
This graduate seminar focuses on research methodologies, academic writing, literature review techniques, and ethical considerations in computer science research. Students will develop research proposals and critique published works.
Learning Objectives
- Understand the research process in computer science
- Critically evaluate research papers and methodologies
- Develop skills for academic writing and research presentations
- Design and propose original research projects
- Understand ethical considerations in research
Assessments
- Research proposal (30%)
- Paper reviews and critiques (25%)
- Research presentation (20%)
- Participation in discussions (15%)
- Peer reviews (10%)
Course Details
Credits: 3
Schedule: Friday 14:00-17:00
Location: Room H412
Prerequisites: Graduate standing
Materials: All readings provided online
Download SyllabusCourse Description
This graduate-level course explores modern artificial intelligence techniques with a focus on deep learning. Students will learn both theoretical foundations and practical implementations of neural networks with applications in computer vision, natural language processing, and more.
Learning Objectives
- Understand core concepts of artificial intelligence and deep learning
- Implement various neural network architectures using PyTorch
- Apply deep learning to real-world problems
- Evaluate and compare model performance
- Understand current research trends in AI
Assessments
- Programming assignments (40%)
- Midterm exam (20%)
- Final project (30%)
- Participation and quizzes (10%)
Course Details
Credits: 3
Schedule: Mon/Wed 10:00-11:30
Location: Room H245
Prerequisites: Machine Learning, Python programming
Textbook: Deep Learning by Goodfellow, Bengio, and Courville
Download SyllabusCourse Description
This course covers data mining techniques and their applications to large-scale datasets. Topics include association rule mining, clustering, classification, and data preprocessing. Students will work with real-world datasets using popular data mining tools and libraries.
Learning Objectives
- Understand the principles of data mining
- Apply various data mining algorithms to real-world problems
- Evaluate and compare model performance
- Preprocess and clean data for analysis
- Implement data mining solutions using Python and R
Course Details
Credits: 3
Prerequisites: Statistics, Database Systems
Textbook: Data Mining: Concepts and Techniques by Han, Kamber, and Pei
Undergraduate Courses
CS3011: Advanced Algorithm Design
Spring 2025 | Tue/Thu 13:30-15:00 | Room H534
This undergraduate course covers advanced algorithms and computational complexity theory. Students will analyze algorithm efficiency, design advanced algorithms, and explore optimization techniques.
Topics Covered
- Divide and conquer algorithms
- Dynamic programming
- Greedy algorithms
- Network flow algorithms
- NP-completeness
- Approximation algorithms
- Computational geometry
- String algorithms
CS1001: Introduction to Programming
Fall Semesters
Introductory programming course using Python, focusing on problem-solving and computational thinking. No prior programming experience required.
CS2011: Data Structures and Algorithms
Spring Semesters
Comprehensive coverage of fundamental data structures (arrays, linked lists, stacks, queues, trees, graphs) and basic algorithms with implementation in Java.
CS3022: Database Systems
Fall Semesters
Principles of database design and implementation. Topics include ER modeling, relational algebra, SQL, normalization, and transaction processing.
CS4015: Mobile Application Development
Fall Semesters
Design and implementation of mobile applications for Android platform. Topics include UI design, services, data storage, and publishing.
Teaching Resources
Student Testimonials
"The AI and Deep Learning course was challenging but incredibly rewarding. Professor's hands-on approach helped me understand complex concepts and apply them to real-world problems."
Graduate Student
AI and Deep Learning, 2024"The Advanced Algorithm Design course gave me a strong foundation in algorithmic thinking. The professor's clear explanations and challenging assignments prepared me well for my software engineering career."
Undergraduate Student
Advanced Algorithm Design, 2023"Research Methods was one of the most valuable courses I took in the graduate program. It provided me with the knowledge and skills needed to conduct high-quality research and write academic papers."
