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 |
|---|---|---|---|---|---|---|
| 10907 | LEC/STUDIO | SCL4 | WED | 1430-1620 | ONLINE | Teaching Wk3 |
| 10907 | LEC/STUDIO | SCL4 | WED | 1430-1620 | LT23 | Teaching Wk1,2,4-13 |
| 10907 | TUT | SCEL | MON | 1230-1320 | ONLINE | Teaching Wk3 |
| 10907 | TUT | SCEL | MON | 1230-1320 | LT27 | Teaching Wk2,4-13 |
0930
1030
1130
1230
1330
1430
1530
1630
1730
CE4041
TUT | ONLINE
Teaching Wk3
CE4041
TUT | LT27
Teaching Wk2,4-13
CE4041
LEC/STUDIO | ONLINE
Teaching Wk3
CE4041
LEC/STUDIO | LT23
Teaching Wk1,2,4-13
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