Postgraduate Courses

On successful completion of the course, students will be able to:

• 1.
  Develop advanced Python programming skills, including the ability to manipulate and analyze large datasets, visualize data, and build and train neural network models.
• 2.
  Gain proficiency in using popular AI libraries and frameworks.
• 3.
  Learn how to deploy AI models for real-world applications.

On successful completion of the course, students will be able to:

• 1.
  Identify real-world problems where artificial intelligence techniques can be applied and propose appropriate solutions.
• 2.
  Apply a wide range of artificial intelligence techniques to solve problems.
• 3.
  Design intelligent systems that can learn from experience and make decisions based on data.

On successful completion of the course, students will be able to:

• 1.
  Develop a deep understanding of the theoretical foundations of representative machine learning models and algorithms for different learning paradigms.
• 2.
  Apply machine learning models and algorithms to solve real-world problems.
• 3.
  Evaluate the performance of machine learning models and algorithms using appropriate metrics and techniques.
• 4.
  Design and implement machine learning models and algorithms for various application sectors.

On successful completion of the course, students will be able to:

• 1.
  Identify ethical issues that arise in the design, development, and use of artificial intelligence systems.
• 2.
  Evaluate the fairness and bias of artificial intelligence systems and propose ways to mitigate these issues.
• 3.
  Analyze the impact of artificial intelligence on society and the workforce and propose ethical guidelines for its development and deployment.
• 4.
  Develop ethical frameworks for the governance and regulation of artificial intelligence.

On successful completion of the course, students will be able to:

• 1.
  Develop a deep understanding of computer vision and its applications in the field of artificial intelligence.
• 2.
  Gain proficiency in building and training popular machine learning models for a wide range of computer vision tasks.
• 3.
  Integrate conventional and machine learning techniques to solve computer vision problems in real-world applications.
• 4.
  Apply critical thinking and problem-solving skills to design and implement computer vision systems that meet specific requirements and constraints.

On successful completion of the course, students will be able to:

• 1.
  Develop a deep understanding of natural language processing and its applications in the field of artificial intelligence.
• 2.
  Gain proficiency in building and training popular machine learning models for a wide range of natural language processing tasks.
• 3.
  Integrate conventional and machine learning techniques to solve natural language processing problems in real-world applications.
• 4.
  Apply critical thinking and problem-solving skills to design and implement natural language processing systems that meet specific requirements and constraints.

On successful completion of the course, students will be able to:

• 1.
  Identify the fundamental concepts of foundation models and generative artificial intelligence.
• 2.
  Interact effectively with large language and multimodal models through prompting for a wide range of tasks in real-world applications.
• 3.
  Integrate foundation models into AI application development.
• 4.
  Apply critical thinking and problem-solving skills to design and implement generative artificial intelligence systems that meet specific requirements and constraints.

On successful completion of the course, students will be able to:

• 1.
  Identify the fundamental concepts and techniques of reinforcement learning.
• 2.
  Apply different reinforcement learning algorithms to solve real-world problems.
• 3.
  Evaluate the performance of different reinforcement learning models for a variety of tasks.
• 4.
  Develop and implement end-to-end reinforcement learning systems for real-world applications.
• 5.
  Critically analyze and compare different approaches to reinforcement learning.

On successful completion of the course, students will be able to:

• 1.
  Identify the basic concepts and methods in HCI research.
• 2.
  Identify the foundations and trends of HCI applications.
• 3.
  Design an interactive system using various methods through different design activities.
• 4.
  Prototype an interactive system with assorted digital and physical tools.
• 5.
  Evaluate an interactive system through user studies.
• 6.
  Communicate effectively with target users and different stakeholders in academia and industry.

On successful completion of the course, students will be able to:

• 1.
  Explain the different medical imaging techniques and various medical image analysis tasks.
• 2.
  Acquire the fundamentals of deep learning methods with application to medical imaging and analysis.
• 3.
  Explain and apply the skills of deep learning technologies in medical image analysis tasks.
• 4.
  Identify and explain the state-of-the-art deep learning approaches for medical imaging applications.
• 5.
  Understand the current research and development trends in both academia and industry in the domain of medical imaging and analysis.

On successful completion of the course, students will be able to:

• 1.
  Describe mainstream security issues in different computer systems and applications.
• 2.
  Use AI techniques to solve common security challenges and protect computer systems and applications.
• 3.
  Identify common security and privacy pitfalls in AI and technical solutions to harden AI systems.
• 4.
  Apply critical thinking and problem-solving skills to use AI techniques to solve cybersecurity problems that meet specific requirements and constraints.

On successful completion of the course, students will be able to:

• 1.
  Identify the different AI techniques for various medical analysis tasks.
• 2.
  Acquire the fundamentals of deep learning methods for healthcare applications.
• 3.
  Explain and apply the state-of-the-art of deep learning technologies in medical analysis tasks, including computer-aided detection, diagnosis and prognosis, etc.
• 4.
  Review the current research and development trends in both academia and industry in the domain of artificial intelligence for healthcare.

On successful completion of the course, students will be able to:

• 1.
  Apply fundamental concepts of the automated/AI technologies for software engineering.
• 2.
  Deploy automated/AI tools for fault detection and diagnosis on open-source software.
• 3.
  Evaluate potential issues in adopting automated/AI solutions on real software projects.
• 4.
  Design an appropriate automated/AI solution for a given software engineering task.

On successful completion of the course, students will be able to:

• 1.
  Recognize the major issues and technical problems in a specific topic in artificial intelligence.
• 2.
  Identify key technical solutions to the problems in the topic.
• 3.
  Solve specific problems in the topic.

On successful completion of the course, students will be able to:

• 1.
  Apply project planning and management skills effectively by taking into consideration the characteristics of AI projects.
• 2.
  Develop effective written and oral communication skills to facilitate interdisciplinary collaboration and project reporting.
• 3.
  Develop an understanding of ethical and legal issues in AI.
• 4.
  Demonstrate professionalism and integrity in AI system development.
• 5.
  Conduct feasibility study to facilitate project planning.
• 6.
  Apply advanced AI technologies to solve real-world problems.
• 7.
  Apply project management tools for effective teamwork support.
• 8.
  Design experiments to generate results for project reporting.