Courses

Cognition and Cognitive Process Modelling
Cognitive Science is the study of the mind and human intelligence, including computational models of human thought.
It is the interdisciplinary study of how information, e.g., concerning perception, language, reasoning, and emotion, is represented and transformed in the mind.
It consists of multiple research disciplines, including psychology , artificial intelligence , philosophy , neuroscience , learning sciences , linguistics , anthropology , sociology , and education .
  • Nature of Mind and Mind Body problem.
  • Mechanisms of Mind.
  • Multiplicity of Mind.
  • Evolution Natural and Artificial.
  • The First AI Debate.
  • The Second AI Debate.
  • Representation and Third AI Debate.
  • Neural Networks.
  • Cognitive Models (SOAR/ACT-R/CLARION)


    • Principles of Interaction Design

  • Design methodology for complex products, services and events: Design of integrated systems, products for future use, products to be used in groups, devices used in public places, design of multi-modal interfaces, expressive interfaces, products that enrich user experience.
  • The course takes an inter-disciplinary approach drawing upon product design, visual communication, information architecture, cognitive psychology and computer science. The course involves exploration of alternatives, pushing the envelope of what is known.
  • The focus is on working collaboratively in groups to solve design problems. The course will involve doing projects. Students need to build soft prototypes of proposed systems at the end of the course.


    • Virtual Reality

  • Introduction to early VR technologies.
  • Input Devices: Trackers, Navigatioin & Gesture Interface.
  • Output Devices: Graphics, 3D, Sound & Haptic Displays.
  • Computing architecture for VR.
  • Modelling Techniques.
  • VR Programming.
  • Human Factors in VR.
  • Traditional & Emerging applications of VR.


    • Image and Vision Processing
  • Digital Image Representation.
  • Intensity Transformations and Spatial Filtering.
  • Frequency Domain Processing.
  • Image Restoration.
  • Color Image Processing.
  • Wavelets.
  • Image Compression.
  • Morphological Image Processing.
  • Image Segmentation.
  • Object Recognition.
  • Stereovision.


    • Soft Computing

  • Development and Evaluation of Artificial Neural Network Model for Real life Applications.
  • Hopfield Networks.
  • Decision Trees.
  • Fuzzy Logic and its use in control engineering.
  • Problem solving using fuzzy techniques.
  • Cluster Computing.
  • Genetic Programming: Issues and Applications.
  • Hidden Markov Models.
  • Hybrid Systems of ANN, GA and Fuzzy systems.
    • Advanced Graphics and Animation
  • Simple Raster Graphics.
  • Graphics hardware.
  • Geometrical transformations.
  • Viewing in 3D.
  • Object Heirarchy and simple PHIGS.
  • Dialog Design.
  • Representing curves and surfaces.
  • Solid Modelling.
  • Achromatic and colored lights.
  • Visible surface determination.
  • Illumination and shading.
  • Animation Production and Techniques.
  • Interpolation and bsic techniques.
  • Natural Phenomena.
  • Rendering Issues.


    • Natural Language Processing

  • Introduction to course.
  • Morphological Segmentation.
  • Language Models and Word prediction.
  • Part of Speech Tagging.
  • Parsing.
  • Pronoun Resolution.
  • Named Entity Recognition.
  • Word Sense Disambiguation.
  • Information Retrieval.
  • Sentiment Analysis.
  • Natural Language Generation.
  • Document Summarization
  • Machine Translation
  • Question Answering


    • Computational Intelligence

  • CI Paradigms.
  • Artificial Neural Networks.
  • Supervised Learning Neural Networks.
  • Unsupervised Learning Neural Networks.
  • Radial Basis Functions.
  • Reinforcement Learning.
  • Evolutionary Computation.
  • Genetic Algorithms.
  • Fuzzy sets, logic, reasoning, controllers.


    • Information Retrieval

  • Boolean retrieval.
  • The term vocabulary & postings lists.
  • Dictionaries and tolerant retrieval.
  • Index construction.
  • Index compression.
  • Scoring, term weighting & the vector space model.
  • Computing scores in a complete search system.
  • Evaluation in information retrieval.
  • Relevance feedback & query expansion.
  • XML retrieval.
  • Probabilistic information retrieval.
  • Language models for information retrieval.
  • Text classification & Naive Bayes.
  • Vector space classification.
  • Support vector machines & machine learning on documents.
  • Flat clustering.
  • Hierarchical clustering.
  • Matrix decompositions & latent semantic indexing.
  • Web search basics.
  • Web crawling and indexes.


    • Wireless Sensor Network

  • Overview Of Wireless.
  • Sensor Networks.
  • Architectures.
  • Networking Sensors.
  • Infrastructure Establishment.
  • Sensor Network Platforms And Tools.