This course introduces the fundamental concepts and methods in pattern recognition and machine learning. Topics covered include:
- Introduction, probability review
- Bayesian Inference
- Mixture Models and EM Algorithm
- Markov Models and Hidden Markov Models
- Sampling
- Markov chain Monte Carlo (MCMC)
- Neural Networks
- Deep Learning (CNN)
- Training Deep Networks
- Deep Network Architectures
- Applications
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | EE2006, IM2006, IE2106, MH2500 |
| Exam | 30 November 2026, 1.00 pm - 3.00 pm |
The Exam information shown may be subject to changes. Students are to check the finalised exam timetable with exam seat information, which will be available at the 'Examination Seating Arrangement' webpage, 2 weeks before start of examination.
Prerequisite Graph
Required first
EE2006Engineering Mathematics IIE2106Engineering Mathematics IIM2006Engineering Mathematics IMH2500ProbabilityPattern Recognition & Deep Learning
Unlocks
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 1900 | COMMON LEC (EPLE) 1900-2150 Mon "LT1A" | ||||
| 1930 | |||||
| 2000 | |||||
| 2030 | |||||
| 2100 | |||||
| 2130 |
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