NCHU Course Outline
Course Name (中) 數據分析與機器學習(5154)
(Eng.) Data Analysis and Machine Learning
Offering Dept Department of Mechanical Engineering
Course Type Elective Credits 3 Teacher Bluest Lan
Department Department of Mechanical Engineering/Undergraduate Language 中/英文 Semester 2024-FALL
Course Description Machine learning is bound up with artificial intelligence and its applications. This course provides an overview of the basic concepts related to data analysis, aiming at developing essential machine learning and data science skills.
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
At the conclusion of this subject students should be able to:
1. Describe the concepts of machine learning algorithms
2. Analyse and select appropriate approaches for real problems
1.The ability to apply the knowledge of math, science, and mechanical engineering.
2.The ability to design and conduct experiments, as well as to analyze the data obtained.
3.The ability to work with others as a team to design and manufacture products of mechanical engineering systems.
4.The ability humanities awareness and a knowledge of contemporary issues, and to understand the impact of science and engineering technologies, environmental, societal, and global context.
5.The ability of continuing study and self-learning.
6.The knowledge of professional ethics and social responsibilities of a mechanical engineer.
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20
20
20
10
10
Visit
topic Discussion/Production
Discussion
Practicum
Lecturing
Written Presentation
Attendance
Oral Presentation
Study Outcome
Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Basics of Machine Learning and Data Analysis
Week 2 Supervised and Unsupervised Learning
Week 3 Introduction to Python
Week 4 Linear Regression and its Applications
Week 5 Decision Trees and Random Forests
Week 6 Classification: Logistic Regression and SVM
Clustering with K-means
Week 7 Dimensionality Reduction and PCA
Feature Engineering and Selection
Week 8 Overfitting, Regularisation and Cross-Validation
Week 9 Introduction to Neural Networks
Week 10 Natural language processing
Week 11 Industry Insight
Week 12 Practical
Week 13 Practical
Week 14 Practical
Week 15 Practical
Week 16 Final Presentation
Week 17 Review
Week 18 Review
Evaluation
Quiz (30%); Final Project (70%)
Textbook & other References
Zaki, Data Mining and Machine Learning 2e. Cambridge University Press.
Teaching Aids & Teacher's Website
iLearning
Office Hours
Thursday 13.00 - 14.00
Sustainable Development Goals, SDGs
08.Decent Work and Economic Growth   09.Industry, Innovation and Infrastructure   17.Partnerships for the Goalsinclude experience courses:Y
Please respect the intellectual property rights and use the materials legally.Please repsect gender equality.
Update Date, year/month/day:None Printed Date, year/month/day:2024 / 10 / 11
The second-hand book website:http://www.myub.com.tw/