Intelligent User Interface – Course Structure







The first lecture presents objectives and scopes of Intelligent User Interface (IUI), the course structure and important conferences and journals in IUI.


Lecture 1: Introduction [Lecture notes] 



This section consists of five lectures on basics of Artificial Intelligence, Machine Learning, Information Retrieval and Signal and Image Processing. We shall highlight applications of these subjects in developing IUI.


Lecture 2: Heuristic Search Algorithms, Complexity Analysis, State Space Modelling, Application on developing IUI for Automotive UI [Lecture notes]

Lecture 3: Uncertainty Modelling, Conditional Probability, Bayes’ Rule, Certainty Factor, Expert System, Training on CLIPS Expert System in .Net Framework [Lecture notes] [Expert System Example]

Lecture 4: Bayesian Inferencing, Bayesian Network, Naïve Bayes Classifier [Lecture notes] [Case Study]

Lecture 5: Information Retrieval, Precision and Recall, Link analysis, HITS and Page Rank Algorithms [Lecture notes]

Lecture 6: Signal and Image Processing, Shape Recognition, Application on developing inspection tool for smart manufacturing [Lecture notes]

Lecture 7: Introduction to Machine Learning, Classification and Clustering, Backpropagation Neural Network, Cluster Validation Index, Case study on predicting pointing target, Training on using WEKA Machine Learning Toolbox [Lecture Notes] [Neural Network Example]


Adaptive Interface

This section presents three lectures on developing adaptive interface and interaction techniques. Applications will include designing intelligent communication aid for children with severe speech and motor impairment, developing smart TV for elderly users and improving human machine interaction for drivers and military aircraft pilots.


Lecture 8: Static and Dynamic Adaptation of interfaces, application on developing assistive interface [Lecture notes]

Lecture 9: User profile and content adaptation, User modelling, Application on Smart TV [Lecture notes]

Lecture 10: Cursor smoothing, filtering techniques, Kalman filter, real time polynomial curve fitting, Target prediction, Application from Automotive UI and Assistive Interface [Lecture notes]


New Modalities

Here we shall present novel interaction technologies and hands on training on developing programs using freely downloadable software development kits (SDK) for multiple input and output modalities.


Lecture 11: Gesture Recognition, Training on developing applications using Microsoft Kinect and Leap Motion, Applications to consumer electronics, Smart TV, Automotive UI [Lecture notes]

Lecture 12: Speech Recognition, Basics on Hidden Markov Model, Training on developing applications using Microsoft Speech SDK [Lecture notes]

Lecture 13: Gaze Control interface, Training on developing application using Tobii SDK, Application to assistive and military aviation interface [Lecture notes]

Lecture 14: Augmented and Virtual Reality Systems, Applications from smart manufacturing, maintenance and inspection [Lecture notes]

Lecture 15: Multimodal interaction, combining multiple i/o modalities, applications from aviation and automotive UI and digital TV [Lecture notes]


Usability evaluation

This section explains designing experiments and user trials, using different usability metrics and doing statistical analysis of results.


Lecture 16: Experiment Design suitable for IUI – conducting repeated measure ANOVA / H-Test, Regression Analysis, Post-Hoc Analysis [Lecture notes]


Course Structure

Students’ Projects