Historical snapshot — AY2022/2023 Semester 2 · View current offering →

Pattern Recognition Deep Learning

AY2022/2023 Semester 2

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
Categories	CoreBDE
Not Available To Programme	ACBS, ACC, ADM, ARED, BCE, BCG, BEEC, BIE, BMS, BS, BSB, BSPY, BUS, CBE, CBEC, CE, CEE, CEEC, CHEM, CHIN, CNEL, CNLM, CS, CSC, CSEC, CVEC, DSAI, ECMA, ECON, ECPP, ECPS, EESS, ELAH, ELH, ELHS, ELPL, ENE, ENEC, ENG, ESPP, HIST, HSCN, HSLM, LMEL, LMPL, LMS, MACS, MAEC, MAEO, MAT, MATH, ME(DES), ME(NULL), ME(RMS), MEEC(DES), 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, SSM
Not Available As BDE/UE To Programme	EEE, EEEC, IEEC, IEM, REP(EEE)
Exam

Available Indexes

	Mon	Tue	Wed	Thu	Fri
930				COMMON LEC (EELE) 0930-1220 Thu LT10
1000
1030
1100
1130
1200