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Historical snapshot — AY2024/2025 Semester 2 · View current offering →
ModsIE4483AY2024/2025 Semester 2

Artificial Intelligence Data Mining

AY2024/2025 Semester 2

This course aims at introducing you to the fundamental theory and concepts of Artificial intelligence (AI) and Data Mining methods, in particular state space representation and search strategies, association rule mining, supervised learning, classifiers, neural networks, unsupervised learning, clustering analysis, and their applications in the area of AI and Data Mining. This can be summarized as: (a) To understand the concepts of knowledge representation for state space search, strategies for the search. (b) To understand the basics of a data mining paradigm known as Association Rule Mining and its application to knowledge discovery problems. (c) To understand the fundamental theory and concepts of supervised learning, unsupervised learning, neural networks, several learning paradigms and its applications. Contents: Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis.

AUs3.0 AUs
CategoriesCore
Not Available To ProgrammeACBS, 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 ProgrammeEEE, EEEC, IEEC, IEM, REP(EEE)
Exam

Available Indexes

MonTueWedThuFri
1830

COMMON LEC (EEE)

1830-2120 Thu

"LT27"

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2100