Courses Offered:
CE7402:
Performance Modelling & Simulation of Queuing
Systems
This course provides an in-depth
discussion on the theory and techniques of performance
evaluation of computer systems such as networks, distributed
systems, and information retrieval systems. Both simulation
modelling and analytical modelling are covered. The
modelling techniques based mainly on queuing systems
are discussed. The MODSIM simulation language is used
to explain the concepts of simulation modelling.
Course Synopsis
Stochastic processes and random
variables; Probability distributions; Markov chains;
Birth-Death queuing systems; M/G/1 queue; Priority
Queuing; Mean Value Analysis; Development of simulation
models - formulation, calibration and validation of
models; Traffic modeling; Simulation of sample systems
- computer systems, packet switching networks and
ATM networks.
Total number of lecture hours:
39 hours
CE7405:
Data Mining
This course provides an introduction
to the current state of the art in data mining, covering
the ideas and technologies from many different fields
that are used in data-mining. It also introduces the
software tools commonly available for solving data-mining
problems.
Course Synopsis
Data Warehousing. Data Mining as
a Process. Statistical Evaluation. Data Preparation:
Transformations, Missing Data, Time-Series Data, Text.
Data Reduction. Prediction Methods: Statistical, Neural,
Trees, Rules. Solution Analyses. Specialized Mining:
Text Mining, Web Mining, Distributed Data Mining.
Case Studies.
Total number of lecture hours:
39 hours
CE7427: Advanced Topic
in Cognitive Neuroscience and Embodied Intelligence
This course considers neurological, psychological,
and structural models of intelligence. It uses
these models as a basis for discussion and development
of new models that may exhibit potential for creating
embodied intelligence. The emphasis in this
course is on the development of the concept of self-organizing,
learning neural systems with locally interconnected
processing components (neurons and minicolumns)
Course Synopsis
- Foundations of Embodied Intelligence
- Autonomous Intelligent Systems
- Neurons and How they Work
- Cognitive Neuroscience
- Structures and Learning Simulations
- Hebbian Learning
- Self-Organization
- Universal Learning
- Information Processing by Brain
- Perception and Attention
- Memory
- Language
- Cognition
Total number of lecture hours: 39 hours
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CE7428:
Special Advanced Topic - Computer and Network Security
This
course provides the fundamental concepts, theory and
techniques of Cryptography and Network Security.
Various security issues in computer communications
and applications will also be introduced.
Course
Synopsis
Finite
Fields; Number Theory; Classical Cryptography; Symmetric
Cryptography; Asymmetric cryptography; Modern Cryptography;
Key Exchange schemes; Hash Functions; Digital signatures
and Authentications; Vulnerabilities and Counter Measures;
Applications Security (System Security, PGP, SSL,
IPsec, Kerberos, Firewalls etc).
Total number of lecture hours:
39 hours
CE7429:
Special Advanced Topic - Computational Intelligence
: Methods & Applications
Computational Intelligence (CI)
is based on inspiration from statistical, pattern
recognition, neural network, machine learning, fuzzy
logic, evolutionary computing, scientific visualization
and other sources. This course covers basic theory,
the use of two software packages implementing many
CI algorithms (WEKA and GhostMiner), and examples
of practical applications of CI methods to data in
technical, medical and bioinformatics domains.
Course Synopsis
CI overview, types of adaptive
systems, learning and applications; Visualization
and exploratory data analysis: few variables, Principal
Component Analysis (PCA), Multidimensional Scaling
(MDS), Self-Organized Mappings (SOM), parallel coordinates
and other visualization algorithms; Theory: overview
of statistical approaches to learning, bias-variance
decomposition, expectation maximization algorithm,
model selection, evaluation of results, ROC curves;
Introduction to WEKA and GhostMiner software packages,
presentation of algorithms available in these packages;
Statistical algorithms: discriminant analysis - linear
(LDA), Fisher (FDA), regularized (RDA), probabilistic
data modelling, kernel methods; Similarity based methods,
generation of prototypes, similarity functions, separability
criteria, structural approaches in pattern recognition;
Improving CI models: boosting, stacking, ensemble
learning, meta-learning, information theory for selection
of features.
Total number of lecture hours:
39 hours
CE7490:
Special Advanced Topic - Distributed Systems
This course introduces graduate students to the advanced
topics in distributed computing models, algorithms,
and software systems. It deals with design issues,
implementation techniques, and software tools and
environments to support the implementation of distributed
systems, and aims to prepare graduate students to
carry out research in distributed systems.
Course Synopsis
A selection of topics from the following list will
be covered:
• Distributed computing models and algorithms
• Internet and Web technologies and applications
• Distributed collaboration systems
• Distributed simulation and virtual environments
• Grid computing and P2P systems
• Other distributed systems
Case studies on real distributed systems will be
conducted, and the recent research literature in the
subject area will be reviewed.
Total number of lecture hours: 39 hours
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for more information.
CE7491:
Special Advanced Topic – Digital Image Processing
This course provides the fundamental concepts, theory
and techniques of image processing. Both theoretical
and practical aspects are covered and selected problems
analysed in depth. The course is accompanied by a
laboratory component based on Matlab.
Course Synopsis
Image formation; Enhancement; Edge
detection; Interpolation; Colour processing; Frequency
domain processing; Restoration and reconstruction;
Wavelet processing; Image and video compression; Morphological
operations; Image segmentation and representation;
Object recognition
Total number of lecture
hours: 39 hours
