Doctor of Philosophy in Artificial Intelligence

Prerequisite: Grad Standing

This advanced course builds upon fundamental machine learning concepts to explore state-of-the-art deep learning techniques and advanced machine learning methods. The course covers deep neural networks architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative models and reinforcement learning. Topics include transfer learning, attention mechanisms, generative adversarial networks (GANs), variational autoencoders (VAEs), meta-learning, and explainable AI (XAI). Students will gain hands-on experience implementing advanced models using modern deep learning frameworks and applying them to real-world problems in computer vision, natural language processing, and other domains.

Prerequisite: Grad Standing

This course provides comprehensive mathematical and statistical foundations for the program. It builds upon fundamental concepts in linear algebra, probability theory, basic statistics, and optimization. The course overviews basic and advanced topics that are frequently encountered in computer science applications. The students will learn the basic matrix operations and types, probability models and sampling distributions, statistical inference, regression, and correlation analysis, supervised and unsupervised probabilistic learning. The student will also learn the principles and methods of optimization.

Prerequisite: Grad Standing

This course provides an advanced, research-oriented study of AI-driven data science and big data analytics, focusing on scalable algorithms, distributed systems, and modern artificial intelligence techniques for analyzing large datasets. It covers foundational big data infrastructures such as HDFS, MapReduce, and Spark, followed by data science methodologies for research. The course explores frequent scalable itemset mining and AI-based clustering techniques. Advanced topics include distributed machine learning, scalable deep learning with evaluation at scale, stream analytics, large-scale graph analytics, and knowledge graphs. Students engage in independent research culminating in individual project presentations, emphasizing critical analysis, experimental rigor, and state-of-the-art AI solutions for big data challenges.

Prerequisite: Grad Standing

Prerequisite: QE Panel Approval

This is a 3-credit-hour course intended to hone students’ skills and professional development for undertaking advanced research-oriented tasks in the field of Artificial Intelligence. Students will sharpen their competencies through knowledge exchange in a collaborative scholarly environment, such as seminars and group discussions focused on contemporary AI topics, methodologies, and applications. They will also learn from peers to acquire, analyze, critique, and present research ideas related to AI theories, algorithms, systems, and emerging technologies within a collaborative research setting.

Every PhD student must pass a Comprehensive Examination designed to evaluate the breadth and depth of the student’s knowledge of his or her discipline, as well as the student’s scholarly potential. The comprehensive exam consists of a written exam that will be prepared, administered, and evaluated by an examination committee from the computer science department. Students taking the comprehensive exam must be in good academic standing and complete the required coursework. The Comprehensive Exam consists of three written exams covering core topics. One core topic, and two subjects are selected by the PhD student in consultation with their PhD academic advisor.

Prerequisite: 1501893 PhD Qualification Exam

Students must undertake and complete independent theoretical and/or practical research under the supervision of a faculty member. Students are required to submit a thesis documenting their research and defend it in an oral examination before a committee. The Thesis work should provide the student with advanced knowledge in Artificial Intelligence subjects with an in-depth research experience. Students are required to produce at least two journal papers (Scopus Indexed) of their work before defending the Thesis.

Prerequisite: 1501330 or equiv.

This course involves selected topics in Artificial Intelligence (AI). The course explores advanced/specialized topics in Artificial Intelligence that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering. This advanced graduate course explores in depth several important topics in modern Artificial Intelligence. The main topic list may include intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction problem, Bayesian networks, decision networks, and reinforcement learning. In addition, advanced topics will be covered from the following fields: machine learning, natural language processing, computer vision, robotics, and deep learning. We will supplement the lectures with paper discussions and there will be a significant research project component to the class to learn current research issues.

Prerequisite: 1501701 Statistical Learning Theory and Advanced AI Analytics

This course provides a broad coverage of the field of Natural Language Processing (NLP) through the study of the models, methods, and algorithms of NLP for common NLP problems. Topics include regular expressions, n-gram language models, naive Bayes, sentiment classification; lexicon for sentiments, vector semantics and embeddings, Neural networks, part-of-speech tagging; sequence processing, machine translation, transfer learning with contextual embeddings and pre-trained language models, question answering, and multi-label text classification

Prerequisite: Grad Standing

This course provides a rigorous and in-depth exploration of modern deep reinforcement learning (DRL), covering foundational theory, advanced algorithms, and cutting-edge applications. The course begins with the fundamentals of Markov Decision Processes (MDPs) and classical reinforcement learning (RL) methods, then progresses to deep Q-learning, policy gradient algorithms, actor-critic methods, and model-based approaches. It also covers advanced topics such as multi-agent RL, offline RL, decision transformers, and the integration of RL with large language models (LLMs) through Reinforcement Learning from Human Feedback (RLHF). Emphasis is placed on both theoretical understanding and practical implementation using state-of-the-art libraries and environments.

Prerequisite: 1501771 or equiv.

This is a special topics course. The topics course usually introduces advanced/specialized areas that are not currently offered in regular courses. The topics depend on the interest of the instructor and contents vary at each offering. The following course description followed by the weekly topics is a specific sample for this particular course. Introduction to the basic and advanced concepts and techniques in computer vision. After completing this course, the students will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. The topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking and motion analysis, gesture recognition, image formation and camera models, and stereo vision. The course will cover the deep learning concepts with introduction to various architectures and their applications.

Prerequisite: 1501732 Advanced Machine Learning

This course provides an in-depth exploration of advanced topics in robotics and autonomous systems, emphasizing recent research advances, theoretical foundations, and emerging paradigms. Students will study state-of-the-art methods in robot learning, including reinforcement learning, imitation learning, transfer learning, and agentic AI for autonomous decision-making. The course covers advanced perception and sensing techniques such as 3D vision, multi-modal sensor fusion, simultaneous localization and mapping (SLAM), and active perception in dynamic environments. Students will also examine multi-robot systems, swarm intelligence, collaborative robotics, and distributed coordination algorithms. Human-robot interaction, safe autonomous operations, and ethical considerations are analyzed within the context of current research challenges. Emphasis is placed on critical evaluation of seminal and recent literature, formulation of research questions, experimental design, and rigorous performance assessment. The course culminates in an independent research project, enabling students to contribute novel insights to the field of robotics and autonomous systems, with potential for publication.

Prerequisite: 1501732 Advanced Machine Learning

This advanced graduate course explores in depth several important topics in modern biomedical image processing. The course will emphasize both practical and theoretical aspects of medical image analysis, including image reconstruction, segmentation, registration, and quantitative analysis. Appropriate areas include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound imaging, nuclear medicine, microscopy, image-guided interventions, and deep learning applications in medical imaging. The course will integrate critical paper discussions with lectures and include a substantial research project component to explore contemporary research challenges.

Prerequisite: 1501732 Advanced Machine Learning

This course covers the theoretical foundations and advanced applications of Quantum Artificial Intelligence (QAI). It integrates quantum computing principles with modern artificial intelligence methodologies, focusing on quantum machine learning, variational quantum algorithms, quantum optimization, and hybrid quantum-classical models. The course emphasizes research-driven learning, critical evaluation of current literature, and development of original doctoral-level contributions in QAI.

Prerequisite: 1501701 Statistical Learning Theory and Advanced AI Analytics

This course provides an in-depth study of Probabilistic Graphical Models (PGMs) as a unifying framework for modeling and reasoning with uncertainty in artificial intelligence. Building on the mathematical foundations of probability and optimization, the course covers representation, inference, and learning in both directed (Bayesian Networks) and undirected (Markov Networks) graphical models. Students will explore exact inference algorithms, approximate inference methods (such as sampling and variational inference), and techniques for learning model parameters and structure from data. The course emphasizes both theoretical understanding and practical applications in domains such as computer vision, natural language processing, and decision support systems

Prerequisite: Grad Standing

This course develops the mathematical and algorithmic foundations of autonomous decision making in uncertain, partially observable, dynamic environments. It covers probabilistic state estimation, sequential decision processes, robust and risk-sensitive optimization, planning under uncertainty, and multi-agent interaction. Students will connect theory to practice through implementation, critical reading of recent papers, and a research-oriented final project.

Prerequisite: Grad Standing

The course will explore the advanced optimization methods of AI and ML systems from mathematical basis to evolutionary, swarm-based, Bayesian, and surrogate-assisted approaches. It will cover gradient and second-order methods, large-scale optimization, coevolutionary systems, constrained and multi-objective optimization, and the use of hybrid algorithms to tackle complex, non-convex, and black-box problems. The course aims to facilitate students understanding current methodological advances and tap into the areas of outstanding challenges in the field. This will be achieved through technical lectures that are blended with critical discussions of the recently published research papers.

Prerequisite: Advanced Design and Analysis of Algorithms (1501750) or Equivalent

This course covers advanced data structures and algorithms to solve fundamental computing problems and shows the role of data structures in algorithm design and the use of amortized complexity analysis to determine how data structures affect performance. It covers advanced methods and techniques for designing algorithms using appropriate data structures, proving their correctness, and analyzing their efficiency. Advanced Data Structures such as B-Trees, Fibonacci Heaps, and Data Structures for Disjoint Sets are discussed. Many classical network optimization algorithms, as well as newer and more efficient algorithms selected from the recent technical literature.