| 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 solve engineering problems independently with professional knowledge in mechanical engineering. |
| 2.The ability to think innovatively, to design and conduct researches, as well as to present research outcomes. |
| 3.The ability to manage multi-disciplinary teams and to integrate cross-field technologies.. |
| 4.A broader view of international competition/co-operation of industry. |
| 5.The ability to lead, to manage and to plan life-long learning. |
| 6.The knowledge of professional ethics and social responsibilities of a mechanical engineer. |
|
|
| topic Discussion/Production |
| Discussion |
| Practicum |
| Lecturing |
| Visit |
|
| 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 Goals | include experience courses：Y |
|