This course introduces the fundamental concepts and methods in pattern recognition and machine learning. Topics covered include Introduction, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models and Self-Supervised Learning.
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | EE2006, IM2006, IE2106, CH2010, 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
CH2010Engineering StatisticsEE2006Engineering 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 |
Other offerings
Other Relevant Mods
IE0005
Introduction To Data Science & Artificial Intelligence
IE1005
From Computational Thinking To Programming
IE2104
Digital Electronics
IE2106
Engineering Mathematics I
IE2107
Engineering Mathematics II
IE2108
Data Structures & Algorithms In Python
IE2110
Signals & Systems
IE3012
Communication Principles
IE3014
Digital Signal Processing