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
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | Year 3 standing CE1007, CE1011, CE1107, CE2100 |
| Not Available To Programme | |
| Not Available To All Programme With | (Admyr 2021-onwards) |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | CZ4041, SC4000 |
| Not Offered As BDE | Yes |
| Not Offered As Unrestricted Elective | |
| Exam |
Total hours per week: 3 hrs
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 1230 | 10808 TUT (SCEL) 1230-1320 Mon LT20 Wk2-13 | ||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | COMMON LEC (SCL4) 1430-1620 Wed LT19 | ||||
| 1500 | |||||
| 1530 | |||||
| 1600 |