Courses | Hsu-Chao Lai (賴旭昭)

Data Mining

Tue, 01 Sep 2026 00:00:00 +0000

Course Information

Offered: Spring 2026 and Fall 2026
Level: Undergraduate
Institution: Chang Gung University

Overview

The first half of this course introduces classical data mining problems and algorithms. The second half examines a range of data types commonly encountered in practice and the corresponding mining tasks.

Assignments combine analytical exercises with Python programming tasks to build students’ core problem-solving and implementation skills. For the final project, students work in teams to select a real-world problem from Kaggle, apply techniques learned in class, and present their methods and results in a clear, well-structured report.

Student Feedback

Spring 2026

41 student responses · Positive ratings (Strongly agree + Agree)

5 · Strongly agree 4 · Agree

100%

Teaching Enthusiasm

100%

Student Support

95%

Clarity & Organization

Evaluation item	Strongly agree	Agree	Neutral	Disagree	Strongly disagree
The instructor was consistently punctual and did not leave early or miss class without reason.	98%	2%	0%	0%	0%
The instructor taught with enthusiasm, diligence, and a strong sense of responsibility.	93%	7%	0%	0%	0%
The instructor was readily available to answer questions and discuss course material with students.	90%	10%	0%	0%	0%
The instructor's explanations were clear and well organized.	85%	10%	2%	2%	0%
The instructor's teaching methods stimulated interest and encouraged students to think critically.	78%	20%	2%	0%	0%
The course pacing was well managed and aligned with the syllabus.	90%	10%	0%	0%	0%
The teaching materials effectively supported learning.	83%	10%	7%	0%	0%
The instructor provided sufficient background knowledge and course content.	85%	12%	2%	0%	0%
The grading methods and criteria were clearly explained in advance and were reasonable.	93%	5%	2%	0%	0%

Percent of respondents selecting each option. Values may not total 100% due to rounding.

Fall 2026

Feedback will be added here after the course concludes.

Probability and Statistics

Sun, 01 Feb 2026 00:00:00 +0000

Course Information

Semester: Spring 2026
Level: Undergraduate
Institution: Chang Gung University

Overview

Beginning with fundamental counting principles and Bayesian reasoning, this course guides students in modeling problems in computer science using random variables and probability distributions. It then develops the laws of large numbers and the central limit theorem to reveal the regularities behind large-scale data sampling. Finally, the course connects these foundations to maximum likelihood estimation and regression analysis, establishing core competencies for machine learning and statistical inference.

Student Feedback

Spring 2026

39 student responses · Positive ratings (Strongly agree + Agree)

5 · Strongly agree 4 · Agree

100%

Teaching Enthusiasm

100%

Student Support

95%

Clarity & Organization

Evaluation item	Strongly agree	Agree	Neutral	Disagree	Strongly disagree
The instructor was consistently punctual and did not leave early or miss class without reason.	92%	8%	0%	0%	0%
The instructor taught with enthusiasm, diligence, and a strong sense of responsibility.	97%	3%	0%	0%	0%
The instructor was readily available to answer questions and discuss course material with students.	92%	8%	0%	0%	0%
The instructor's explanations were clear and well organized.	90%	5%	5%	0%	0%
The instructor's teaching methods stimulated interest and encouraged students to think critically.	77%	13%	10%	0%	0%
The course pacing was well managed and aligned with the syllabus.	85%	15%	0%	0%	0%
The teaching materials effectively supported learning.	79%	18%	3%	0%	0%
The instructor provided sufficient background knowledge and course content.	87%	10%	3%	0%	0%
The grading methods and criteria were clearly explained in advance and were reasonable.	92%	8%	0%	0%	0%

Percent of respondents selecting each option. Values may not total 100% due to rounding.

Graph Mining and Learning

Tue, 01 Sep 2026 00:00:00 +0000

Course Information

Semester: Fall 2026
Level: Graduate
Institution: Chang Gung University

Overview

This course equips students with the core principles and practical skills of graph mining and representation learning. Topics span classical algorithms, including PageRank and community detection, modern graph neural networks (GNNs), and the integration of graph structures with large language models (LLMs) to address large-scale, real-world data challenges. In-class presentations and project discussions are also central components of the learning experience.

Student Feedback

Fall 2026

Feedback will be added here after the course concludes.

Courses | Hsu-Chao Lai (賴旭昭)

Data Mining

Course Information

Overview

Student Feedback

Spring 2026

Spring 2026 Course Evaluation

Fall 2026

Probability and Statistics

Course Information

Overview

Student Feedback

Spring 2026

Spring 2026 Course Evaluation

Graph Mining and Learning

Course Information

Overview

Student Feedback

Fall 2026