| 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. |
|
|
| 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 |
Introduction to Data Analysis and Machine Learning
Course overview, supervised vs unsupervised |
| Week 2 |
Linear Regression
Prediction, evaluation, overfitting |
| Week 3 |
Linear and Kernel-Based Classification
Logistic regression, kNN, support vector machines |
| Week 4 |
Model Evaluation and Regularisation
Bias–variance, cross-validation, ridge and lasso |
| Week 5 |
Decision Trees and Ensembles
Splitting, random forests, feature importance |
| Week 6 |
Clustering
Unsupervised grouping, distance measures, evaluating cluster quality |
| Week 7 |
Dimensionality Reduction
PCA, variance, visualisation |
| Week 8 |
Neural Networks
Perceptrons, activation, backpropagation |
| Week 9 |
Natural Language Processing
Tokenisation, bag-of-words, text classification |
| Week 10 |
Exam
Covers Weeks 1–9: concepts, algorithms, and ML implementation |
| Week 11 |
Project Development I
Begin implementation; supervised coding sessions |
| Week 12 |
Project Development II
Continue development; checkpoints and mentoring |
| Week 13 |
Project Development III
Refinement and testing; peer evaluation |
| Week 14 |
Project Development IV
Short updates, debugging support, feedback sessions |
| Week 15 |
Final Presentation I
Formal presentations and oral defence |
| Week 16 |
Final Presentation II
Remaining presentations, peer review, reflection |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
| Quiz (35%); Final Exam (35%); Final Project (30%) |
| 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(Link URL) |
| 04.Quality Education 08.Decent Work and Economic Growth 09.Industry, Innovation and Infrastructure 17.Partnerships for the Goals | include experience courses：N |
|