Not offered in the current semester · Last offered AY2014/2015 Semester 2
Overview: introduction, definition, fundamental concepts, applications. Unsupervised learning: K-means, vector quantization, self-organizing neural networks. Supervised learning: K nearest neighbour, learning vector quantization, decision tree, supervised neural networks. Graphical models: belief networks, Bayesian networks, Hidden Markov models, incremental learning, reinforcement learning, machine learning applications
| AUs | 4.0 AUs |
| Grade Type | |
| Prerequisite | |
| Not Available To Programme | BCE(2011-onwards), BCG(2011-onwards), CE(2011-onwards), CSC(2011-onwards) |
| Not Available To All Programme With | |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | CZ4041 |
| Not Offered As BDE | Yes |
| Not Offered As Unrestricted Elective | |
| Exam |
Total hours per week: 3 hrs
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
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| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |