Deep learning is a class of machine learning algorithms which enables computers to learn from examples. Deep learning techniques have been used successfully for a variety of applications, including automatic speech recognition, image recognition, natural language processing, drug discovery, and recommendation systems.

It will cover the fundamentals of deep learning, and the main research activities in this field. Moreover, students will learn to implement, train, and validate their neural network, and they will improve their understanding of the on-going research in computer vision and multimedia field.

These advanced statistics and methodology courses provide material relevant to the research. Motivate in students an intrinsic interest in statistical thinking. The course dealing with statistical concepts including measures of central tendency and dispersion, probability distributions, the Central Limit Theorem, Sampling, Estimation, Hypothesis Testing, Analysis of Variance, Correlation and Regression analysis, Multiple Regression and Statistical Forecasting. 

It will cover fundamentals of statistical tools, and their implementation in research activities.

This course is designed to cover mid-level security practitioners how to engage all functional levels within the enterprise to deliver information system security. To this end, the course addresses a range of topics, each of which is vital to securing the modern enterprise. These topics include inter alia plans and policies, enterprise roles, security metrics, risk management, standards and regulations, physical security, and business continuity. Each piece of the puzzle must be in place for the enterprise to achieve its security goals; adversaries will invariably find and exploit weak links. It will cover advanced issues in ethical hacking and penetration testing

A seminar course designed to share and develop knowledge of current science and technology trends in industry and research. This course focuses on communication skills and soft skills which are required for IT professionals. Research scholars, as well as the instructor, will be actively involved in running the course/ seminars on a weekly basis. The seminar topics selected should be relevant to the elective course and subject specialization. The seminar topics will be selected with the consent of supervisors.

Cloud Computing and Organizational work culture & Business, Cloud adoption, Relationship with IT Service Management (ITSM), Cloud Service and Support Models, Cloud Service Management Roles (Cloud Management Roles, Service Management Roles, Organizational Roles), Cloud Service Strategy (Cloud Strategy basics, Key Drivers for Adoption, Risk Management Overview), Cloud Service Design, Deployment and Migration (Dealing with Legacy Systems, Services, and Applications, Benchmarking of Cloud Services, Cloud Service Capability Planning, Cloud Service Deployment and onboarding, The Cloud Store), Cloud Service Management (Cloud Service Level Management & Service Assurance, Managing Cloud Service Configurations, Change Management for Cloud Computing Environments, Reacting to demand Cloud Services), Cloud Service Economics (Pricing Models for Cloud Services, Procurement of Cloud Based Services, Cloud Service Charging), Cloud Service Governance(Governance, Cloud Governance Framework, Cloud Governance Considerations), Showing the Value of Cloud Services to the Business(Understanding the Value of Cloud Services, Linking the Value of Cloud Services to Strategy, Measuring the Value of Cloud Services)