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.
| Academic Units | 3 |
| Exam Schedule | Fri May 02 2025 00:00:00 GMT+0000 (Coordinated Universal Time) 09:00-11:00 |
| Grade Type | Letter Graded |
| Department Maintaining | EEE |
| Prerequisites | |
| Not Available to Programme | ACBS, ACC, ADM, AISC, ARED, BACF, BASA, BCE, BCG, BEEC, BIE, BMS, BS, BSB, BSPY, BUS, CBE, CBEC, CE, CEE, CEE 1, CEEC, CHEM, CHIN, CMED, CNEL, CNLM, COMP, CS, CSC, CSEC, CVEC, DSAI, ECMA, ECON, ECPP, ECPS, EEE 1, EESS, ELAH, ELH, ELHS, ELPL, ENE, ENE 1, ENEC, ENG, ESPP, HIST, HSCN, HSLM, LMEL, LMPL, LMS, MACS, MAEC, MAEO, MAT, MATH, ME 1, ME(DES), ME(IMS), ME(NULL), ME(RMS), MEEC(DES), MEEC(IMS), MEEC(NULL), MEEC(RMS), MS(ITG), MS(NULL), MS-2ndMaj/Spec(MSB), MTEC, PHIL, PHY, PLCN, PLHS, PPGA, PSLM, PSMA, PSY, REP(ASEN), REP(BIE), REP(CBE), REP(CE), REP(CSC), REP(CVEN), REP(ENE), REP(MAT), REP(ME), SCED, SOC, SPPE, SSM |
| Not Available as BDE/UE to Programme | EEE, EEEC, IEEC, IEM, REP(EEE) |
| Index | Type | Group | Day | Time | Venue | Remark |
|---|
0930
1030
1130
1230
1330
1430
1530
1630
1730
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