Artificial Intelligence – MAI602
The aim of this course is to provide graduate students with in-depth knowledge of AI principles, algorithms and techniques. Topics covered include Knowledge Representation schemes and Automated Reasoning, uncertain knowledge and probabilistic reasoning, search strategies, intelligent agents, machine learning, planning, and ethical and societal issues relating to artificial intelligence. Students also work on a course project individually or in pairs.
Biomedical informatics – MAI611
This course presents the unique characteristics of biomedical data, clinical data and the representation methods of life science; data, information, and knowledge. All utilized to improve human health and provide wider understanding of biomedical data related to health care. It covers basic concepts and provide solid background for research related to the exploration of computational and analytical aspects of biomedical information systems. It encompasses topics such as information sharing and management, clinical informatics and decision-making, clinical information retrieval, imaging informatics, patient monitoring systems, cognitive science/natural language processing in bioinformatics, and translational applications of informatics. There will be hands-on assignments and term paper to familiarize students with those concepts.
Business Intelligence – MAI610
Business intelligence (BI) is a broad term that includes set of methodologies, processes, and technologies that transform raw data into meaning information and knowledge that can be used by decision makers. This course aims to provide students with broad and in-depth knowledge of Business Intelligence (BI). Topics covered include, basic concepts of BI, Data warehousing concepts and requirements, business performance management, descriptive analytics, predictive analytics, prescriptive analytics, Decision Support Systems, Knowledge Management, Big Data/data mining and text mining, and data visualization. Students participate in the development of group project that embodies the solution of business problem using BI techniques.
Computer Vision and Pattern Recognition – MAI609
This course provides a broad coverage of the fundamental techniques of image processing and computer vision. Course topics include: essential mathematical background, image geometry, image processing operations and filters, edge detection, segmentation, image features extraction, object recognition techniques, and synthesis and analysis. Students also work on group projects (2 to 3 students) to create an object recognition software component or system.
Data Mining – MAI601
Data mining is the process of discovering patterns and knowledge from huge amount of dataset. This course aims to equip students with the necessary skills and knowledge that allow them to develop models using data mining techniques that include association, clustering, outlier, web mining, text mining, and pattern mining approaches. Students will also learn to collate, filter, clean, transform, and sort data using established contemporary tools. Validation and performance assessment is applied to compare test data with training data and assess accuracy of processes and models.
Evolutionary Computations – MAI607
This course will cover topics in evolutionary computations and their application to solve optimization problems. Course topics include: basics of genetic algorithms; constrained optimization; Multimodal Optimization; Multiobjective Optimization; Swarm Intelligence; Genetic Programming; and Combinatorial Optimization. Students also are required to review research on evolutionary computation and work in group projects (2 to 3 students) to solve a problem using evolutionary-based algorithms.
Human Computer Interactions – MAI608
This course provides students with the necessary knowledge and skills needed to design and implement interactive computer systems using the latest human computer interaction (HCI) principles and theories. Topics covered by this course include, general overview of HCI, universal usability, guidelines, principles, and theories of HCI, managing design processes, evaluating design, Interaction styles, devices, communication and collaboration, critical design issues, search and visualization.
Machine Learning – MAI603
This course aims to provide students with an in-depth introduction to the main areas of machine learning. Topics covered include, supervised and unsupervised learning models and algorithms in classification, regression, and clustering, reinforcement learning algorithms and models, genetic algorithms in machine learning, and model selection and evaluation. Students also work in group projects (2 to 3 students) that embodies the solution to a machine learning problem.
Master Project – MAI698
Student is required to plan, design, build, and test a high functionality project in coordination with a project supervisor. The student should use the experience and knowledge gained from preceding courses taken earlier to improvise and build an AI based application that has great potential of being transformed into a commercial asset. Research component is highly recommended in the master project especially if it constituted a heuristic or technological addition. The project requires a written proposal, a proposal presentation, and a final presentation.
Master Thesis – MAI699
Students are required to investigate some contemporary AI related topic, prepare a high quality proposal, and write a high quality manuscript with content and organization that meet international standards. Students should follow the rules and regulations set by the MSAI council for that purpose. All work should be done in coordination with a thesis supervisor. The thesis must constitute original contributions, in the form of theories or heuristics, to the fields of AI and its applications. There will be an oral presentation for the submitted proposal and oral defense for the thesis. Manuscript of the thesis will be archived and copyrighted.
Natural Language Processing with Deep Learning – MAI612
Fundamental concepts of Natural Language Processing (NLP) and deep learning methods are introduced. Word level syntactic and semantic processing from linguistic and algorithmic perspectives are discussed. Statistical acquisition and modern quantitative techniques are presented. Deep learning neural networks paradigms are introduced. Deep Learning techniques in NLP utilizing different learning methods and architectures such as Multi-layer perceptron, convolutional, encoder-decoder, Greedy-Wiser DBN will be used in solving underlying problems. Comparisons with classical methods (HMM/statistical) are presented. Corpuses preparation is presented. A research or application oriented project in text and/or speech recognition will be conducted.
Robotics – MAI605
Methods of analysis for operations of robotics are presented. The manipulators dynamics and kinematics including trajectory planning along with motion control, vision, and sensing are covered. Programming to control robots using hardware interfaces (microcontrollers) for motion and motion planning along with task assembling. Optimum trajectory and optimum grippers are presented. Uncertainty and stability issues in grasping and planning. Applications of robots in several areas of real life. Hybrid AI and robotics techniques. Lab work will provide hands on experience.
Special Topics in Artificial Intelligence – MAI613
The course should demonstrate an in depth understanding of the theories of AI techniques and applications in a relevant field. Students also need to be able assess and critique some research findings in that AI field. They will integrate AI technologies to improvise innovative solutions for some complex problem. During the course, they must function independently and collectively and take full responsibility to complete a research/project development. They should resolve complex ethical issues when implementing AI- based solutions.