| Course Name |
(中) 人工智慧概論(2046) |
| (Eng.) Introduction to Artificial Intelligence |
| Offering Dept |
Department of Business Administration |
| Course Type |
Elective |
Credits |
3 |
Teacher |
Huang Yang |
| Department |
Department of Business Administration/Undergraduate |
Language |
English |
Semester |
2026-FALL |
| Course Description |
This course provides a comprehensive overview of Artificial Intelligence (AI), covering theoretical concepts including Machine Learning, Deep Neural Networks, and a brief introduction to Generative AI. Through the practice of Python and related frameworks (Scikit-Learn, PyTorch, etc.), students are expected to implement ML/DL solutions for problems across various domains. |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. Basic introduction to Artificial Intelligence algorithms and practices.
2. Fundamental Python and Machine Learning packages programming skills
|
| 1.Independent Thinking |
| 2.Professional Knowledge with Applications |
| 3.Creativity |
| 4.English Proficiency |
|
|
| topic Discussion/Production |
| Exercises |
| Discussion |
| Lecturing |
|
| Attendance |
| Oral Presentation |
| Assignment |
| Quiz |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
9/10 Course Overview and Introduction to Artificial Intelligence |
| Week 2 |
9/17 Python Basic Tutorial |
| Week 3 |
9/24 Exploratory Data Analysis |
| Week 4 |
10/1 Introduction to Machine Learning I |
| Week 5 |
10/8 Introduction to Machine Learning II |
| Week 6 |
10/15 Data Preprocessing, Imbalanced Data, Model Performance Evaluation |
| Week 7 |
10/22 Artificial Neural Networks I |
| Week 8 |
10/29 Artificial Neural Networks II |
| Week 9 |
11/5 Final Project Proposal & Discussion |
| Week 10 |
11/12 Midterm Exam |
| Week 11 |
11/19 Computer Vision and Convolutional Neural Networks I |
| Week 12 |
11/26 Computer Vision and Convolutional Neural Networks II |
| Week 13 |
12/3 Natural Language Processing and Large Language Models |
| Week 14 |
12/10 Final Project Presentation |
| Week 15 |
12/17 Final Project Presentation |
| Week 16 |
12/24 Final Project Presentation
|
self-directed
learning |
Python online practice: Codecademy |
|
| Evaluation |
Participation (10%)
Homework (30%)
Mid-term exam (30%)
Individual Final Project (30%) |
| Textbook & other References |
1. I. Goodfellow and Y. Bengio and A. Courville, Deep Learning, The MIT Press, 2016 (http://www.deeplearningbook.org)
2. Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, 2011, by Jiawei Han, Micheline Kamber and Jian Pei
|
| Teaching Aids & Teacher's Website |
| Self-prepared course materials will be uploaded to iLearning before each class. |
| Office Hours |
| Friday 13:00~15:00 |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|