Overview of machine learning and its applications; Decision Theory and Bayes Models; Classifier Evaluation; Classification: Decision trees, artificial neural networks, linear and kernelized support vector machines, K-nearest neighbour classifiers, linear regression and its kernelized extension; Ensemble Learning; Clustering; Dimension Reduction; Density Estimation; Graphical Models; Applications
| Academic Units | 3 |
| Exam Schedule | Not Applicable |
| Grade Type | Letter Graded |
| Department Maintaining | CE |
| Prerequisites | |
| Mutually Exclusive | |
| Not Available to All Programme | (Admyr 2021-onwards) |
| Index | Type | Group | Day | Time | Venue | Remark |
|---|---|---|---|---|---|---|
| 10820 | LEC/STUDIO | SCL4 | TUE | 1030-1220 | ONLINE | Teaching Wk10 |
| 10820 | LEC/STUDIO | SCL4 | TUE | 1030-1220 | LT2A | Teaching Wk1-9,11-13 |
| 10820 | TUT | SCEL | TUE | 1230-1320 | ONLINE | Teaching Wk10 |
| 10820 | TUT | SCEL | TUE | 1230-1320 | LT2A | Teaching Wk2-9,11-13 |
0930
1030
1130
1230
1330
1430
1530
1630
1730
CE4041
LEC/STUDIO | ONLINE
Teaching Wk10
CE4041
TUT | ONLINE
Teaching Wk10
CE4041
LEC/STUDIO | LT2A
Teaching Wk1-9,11-13
CE4041
TUT | LT2A
Teaching Wk2-9,11-13
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