| Course Name |
(中) 大數據分析、機器學習與人工智慧(6749) |
| (Eng.) Big Data Analytics, Machine Learning, and Artificial Intelligence |
| Offering Dept |
Intelligence Science, Engineering and Technology Master Degree Program |
| Course Type |
Elective |
Credits |
3 |
Teacher |
LIAO,KUO-CHIH ect. |
| Department |
Intelligence Science, Engineering and Technology Master Degree Program/Graduate |
Language |
English |
Semester |
2024-FALL |
| Course Description |
This course is designed for students who need hands-on training of data engineering, machine learning, and general understanding of AI. We will begin with introduction to the rise and
evolution of the Data Science. Its applications in various fields, such as business, healthcare, and manufacturing, will also be discussed. |
| 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 |
| This course covers the basic concepts of Big Data Analytics, Data Engineering, Business Analytics, Supervised & Unsupervised Machine Learning, Deep Neural Networks, and Machine Learning Interpretability. Students will also be introduced to how to use R & Python programming language and its packages to solve real-world big data problems. |
| 1.Possess professional knowledge in smart medical devices, smart manufacturing or smart management. |
| 2.Plan and implement research projects, and have the ability to solve problems independently. |
| 3.Ability to write scientific papers and communicate research results effectively. |
| 4.Integration in interdisciplinary research and innovative research skills. |
| 5.Possess insightful perspective on industry and globalization. |
| 6.Capability of leadership, management, planning, communication and lifelong learning. |
|
|
|
| Attendance |
| Assignment |
| Quiz |
| Internship |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction: Big data, Machine learning, AI |
| Week 2 |
Machine Learning: Concepts, Applications |
| Week 3 |
Classifier: Nearest Neighbor |
| Week 4 |
Classifier: Linear and Polynominal |
| Week 5 |
Model: Artificial Neural Network |
| Week 6 |
Model: Decision Tree |
| Week 7 |
Model: Ensemble Learning |
| Week 8 |
Mid-term |
| Week 9 |
Machine Learning: Development process |
| Week 10 |
Big data Analytic: Dataset aspects (feature, label) |
| Week 11 |
Machine learning: Performance Evaluation |
| Week 12 |
Practical: Developing Machine Learning with Colab |
| Week 13 |
Practical: Developing Machine Learning with Colab |
| Week 14 |
Example projects |
| Week 15 |
Example projects |
| Week 16 |
Final Exam |
| Week 17 |
|
| Week 18 |
|
|
| Evaluation |
Attendance: 10%
Assignment: 20%
Mid-term exam: 35%
Final exam: 35%
|
| Textbook & other References |
(1) 張志勇. 人工智慧 三版. 全華圖書. 2023年. ISBN:9786263287037
(2) 蔡炎龍等. 少年Py的大冒險－成為Python AI深度學習達人的第一門課. 全華出版. 2022年. ISBN:9786263282964
(3) Philip C. Jackson Jr. Introduction to Artificial Intelligence. ISBN: 9780486248646 |
| Teaching Aids & Teacher's Website |
| https://www.bme.nchu.edu.tw/members/tsching/ |
| Office Hours |
| Mon 12:00~13:00 |
| Sustainable Development Goals, SDGs |
| include experience courses：N |
|