Candidate graduated from a Doctor of Philosophy (PhD) in Artificial Intelligence (AI) demonstrates a significant and novel contribution to knowledge in the field of AI through the completion of a research thesis. Candidate will be exposed to research training such as knowledge acquisition, critical thinking, programming languages acquisition and analytical skills demonstration in the discipline of AI. Candidate acquires the necessary skills to apply AI in descriptive, predictive and prescriptive analytics to solve decision-making problems.

The program can be completed within :

Full-Time : 6 semester (minimum) – 10 semester (maximum)

Part-Time : 8 semester (minimum) – 14 semester (maximum)

**Students are required to attend at least one course of research methodology (GRU70104) throughout the study and meet the requirements of the audit course. Students may also be required to attend other courses on the Faculty’s proposal and meet the audit course requirements.

– Machine Learning: Researching algorithms and techniques that enable machines to learn and improve from data without being explicitly programmed.

– Natural Language Processing: Studying how to enable computers to understand, interpret, and generate human language, including speech recognition, machine translation, sentiment analysis, and text generation.

– Computer Vision: Exploring methods to enable machines to understand and interpret visual information, such as object recognition, image and video analysis, and scene understanding.

– Robotics: Researching techniques for designing and programming robots to perceive their environment, make decisions, and carry out physical tasks autonomously.

– Knowledge Representation and Reasoning: Investigating methods for representing and organizing knowledge in a form that can be used by AI systems to reason, make decisions, and solve complex problems.

– Multi-Agent Systems: Studying how multiple AI agents can interact, collaborate, and coordinate with each other to achieve specific goals, such as in applications like autonomous vehicles or smart grids.

– Explainable AI: Researching methods to make AI systems more transparent and interpretable, enabling users to understand the underlying reasoning behind their decisions and actions.

– AI Ethics and Fairness: Investigating the societal impacts of AI, including ethical considerations, biases, and fairness in AI algorithms and systems.