MICHAEL
UNIVERSITY
Artificial Intelligence, Ph.D.
Introduction to Artificial Intelligence
Artificial Intelligence (AI) stands at the forefront of technological innovation, representing the pursuit of
creating machines that can mimic human intelligence. This interdisciplinary field combines computer
science, mathematics, engineering, and cognitive science to develop systems capable of performing
tasks that typically require human intelligence. From enhancing productivity in various industries to
revolutionizing how we interact with technology, AI has become an integral part of our modern world.
The roots of AI can be traced back to the mid-20th century when a group of visionary pioneers laid the
foundation for this groundbreaking field. Among the notable founders of AI, some luminaries have left
an indelible mark on its history:
1. Alan Turing (1912–1954): Often hailed as the father of computer science, Alan Turing's contributions
to AI are monumental. His conceptualization of the Turing Machine and the idea of a universal machine
laid the groundwork for computational theory, influencing the development of early AI models. Turing's
seminal work, "Computing Machinery and Intelligence," introduced the famous Turing Test as a measure
of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.
2. John McCarthy (1927–2011): John McCarthy coined the term "Artificial Intelligence" in 1956 and
organized the Dartmouth Conference, which is considered the birthplace of AI as a field of study.
McCarthy's pioneering efforts include the development of the Lisp programming language, a significant
tool in AI research. His work laid the foundation for AI as a distinct discipline, fostering the growth of
symbolic reasoning and problem-solving approaches.
3. Marvin Minsky (1927–2016) and Herbert A. Simon (1916–2001): Minsky and Simon made
groundbreaking contributions to AI, particularly in the areas of cognitive science and problem-solving.
Minsky co-founded the MIT Artificial Intelligence Project, and his book "Perceptrons," co-authored with
Seymour Papert, played a crucial role in shaping the early discourse on neural networks. Simon,
awarded the Nobel Prize in Economics, developed the concept of "bounded rationality" and introduced
the idea of decision-making processes within AI.
4. Norbert Wiener (1894–1964): A mathematician and philosopher, Norbert Wiener's work in
cybernetics, the study of communication and control in living organisms and machines, laid the
groundwork for understanding the interplay between machines and human intelligence. His
interdisciplinary approach influenced the development of AI by emphasizing the feedback and control
mechanisms essential for intelligent systems.
These visionary figures paved the way for the dynamic and ever-evolving field of AI, shaping its
trajectory from theoretical concepts to practical applications that continue to redefine the possibilities
of human-machine collaboration. As we delve into the course on AI, we embark on a journey that
explores the rich history, diverse methodologies, and transformative potential of this revolutionary field.
Welcome to the forefront of intellectual exploration and technological advancement. The Ph.D. Program
in Artificial Intelligence is an immersive journey into the heart of cutting-edge research and innovation,
building upon the legacy of visionaries who laid the foundation for this dynamic field.
As we embark on this intellectual odyssey, we recognize the indelible contributions of pioneers like Alan
Turing, John McCarthy, Marvin Minsky, Herbert A. Simon, and Norbert Wiener, who ignited the spark of
curiosity and set the stage for the evolution of Artificial Intelligence. This program represents an
opportunity to delve into the depths of AI, engaging with the latest theories, methodologies, and
applications that continue to shape the technological landscape.
Our distinguished faculty, comprised of leading researchers and experts in the field, will guide you
through an interdisciplinary exploration of computer science, mathematics, engineering, and cognitive
science. This program is designed for those who seek to push the boundaries of AI, to contribute novel
insights, and to advance the frontiers of human-machine collaboration.
As a Ph.D. candidate, you will have the chance to immerse yourself in groundbreaking research projects,
collaborating with peers and mentors to address complex challenges in AI. From developing innovative
algorithms to exploring ethical considerations and societal impacts, your research will contribute to the
ongoing narrative of AI evolution.
Join us in this transformative journey where intellectual curiosity meets real-world impact. The Ph.D.
Program in Artificial Intelligence is not just an educational pursuit; it is a gateway to becoming a
trailblazer, a thought leader, and a contributor to the ever-expanding realm of possibilities that AI
unfolds. Together, let us shape the future of intelligence.
FEES & CHARGES: BAHAMIAN DOLLARS
Doctor of Philosophy Degree – Total Tuition Fees (3 years): $177,000.00
Tuition per year: $59,000.00
PhD in Artificial Intelligence Program runs for four (3) years unless student is exempted due to previous
knowledge. Official transcripts are required.
Tuition can be paid annually or per semester. Each semester is 12 weeks).
Students who are accepted into Michael University may arrange for financing from various lending
institutions.
Ph.D. Program in Artificial Intelligence Curriculum
Year 1: Foundational Concepts and Theoretical Frameworks
1. AI 701: Introduction to Artificial Intelligence (3 credits)
• Course Description: An overview of the historical development and key concepts in AI. Covers
foundational theories, approaches, and the ethical considerations in AI.
• Course Objectives:
• Understand the historical evolution of AI and its interdisciplinary nature.
• Analyze key AI concepts and methodologies.
• Evaluate ethical implications in AI applications.
Recommended Book: "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
Publisher : Pearson; 4th edition (April 28, 2020)
Language : English
Hardcover : 1136 pages
ISBN-10 : 0134610997
ISBN-13 : 978-0134610993
2. AI 702: Machine Learning and Pattern Recognition (3 credits)
• Course Description: In-depth exploration of machine learning algorithms, statistical modeling,
and pattern recognition. Emphasis on supervised and unsupervised learning techniques.
• Course Objectives:
• Master fundamental machine learning algorithms.
• Apply statistical models to real-world data.
• Develop expertise in pattern recognition techniques.
Recommended Book: "Pattern Recognition and Machine Learning" by Christopher M. Bishop
ASIN : 1493938436
Publisher : Springer; Softcover reprint of the original 1st ed. 2006 edition
(August 23, 2016)
Language : English
Paperback : 798 pages
ISBN-10 : 0241973376
ISBN-13 : 978-1493938438
3. AI 703: Computational Intelligence (3 credits)
• Course Description: Examination of evolutionary algorithms, neural networks, and fuzzy logic.
Practical applications in optimization, learning, and adaptive systems.
• Course Objectives:
• Understand the principles of computational intelligence.
• Implement and analyze evolutionary algorithms.
• Apply neural networks and fuzzy logic to solve real-world problems.
Recommended Book: "Computational Intelligence: A Logical Approach" by David Poole and Alan
Mackworth
ASIN : 0195102703
Publisher : Oxford University Press; 1st edition (January 8, 1998)
Language : English
Hardcover : 576 pages
ISBN-10 : 9780195102703
ISBN-13 : 978-0195102703
Year 2: Advanced Topics and Specialized Applications
4. AI 801: Natural Language Processing and Understanding (3 credits)
• Course Description: Focus on processing and understanding human language using
computational methods. Covers sentiment analysis, language generation, and machine
translation.
• Course Objectives:
• Explore techniques for natural language processing.
• Develop skills in sentiment analysis and language generation.
• Understand challenges in machine translation.
Recommended Book: "Speech and Language Processing" by Dan Jurafsky and James H. Martin
ASIN : 0131873210
Publisher : Prentice Hall; 2nd edition (May 16, 2008)
Language : English
Hardcover : 1024 pages
ISBN-10 : 9780131873216
ISBN-13 : 978-0131873216
5. AI 802: Computer Vision and Image Recognition (3 credits)
• Course Description: Exploration of algorithms for visual perception, image understanding, and
object recognition. Practical applications in image and video analysis.
• Course Objectives:
• Gain proficiency in computer vision algorithms.
• Apply image recognition techniques to real-world scenarios.
• Understand challenges in visual perception.
Recommended Book: "Computer Vision: Algorithms and Applications" by Richard Szeliski
Publisher : Springer; 2nd ed. 2022 edition (January 5, 2022)
Language : English
Hardcover : 947 pages
ISBN-10 : 3030343715
ISBN-13 : 978-3030343712
6. AI 803: Robotics and Autonomous Systems (3 credits)
• Course Description: Study of AI applications in robotics, including perception, motion planning,
and control. Examination of autonomous systems and their integration with AI.
• Course Objectives:
• Analyze AI applications in robotics.
• Develop skills in perception and motion planning.
• Understand principles of autonomous systems.
Recommended Book: "Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox
Publisher : The MIT Press; 1st edition (August 19, 2005)
Language : English
Hardcover : 672 pages
ISBN-10 : 0262201623
ISBN-13 : 978-0262201629
Year 3: Specialization and Research Modules
7. AI 901: Advanced Topics in Reinforcement Learning (3 credits)
• Course Description: In-depth exploration of reinforcement learning algorithms and their
applications in decision-making and control.
• Course Objectives:
• Master advanced reinforcement learning techniques.
• Apply reinforcement learning to complex decision-making scenarios.
• Investigate challenges and recent advancements in reinforcement learning.
Recommended Book: "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G.
Barto
Publisher : Bradford Books; 2nd edition (November 13, 2018)
Language : English
Hardcover : 552 pages
ISBN-10 : 0262039249
ISBN-13 : 978-0262039246
8. AI 902: Ethics and Responsible AI (3 credits)
• Course Description: Examination of ethical considerations in AI, including bias, fairness,
accountability, and transparency. Strategies for developing responsible AI systems.
• Course Objectives:
• Explore ethical challenges in AI applications.
• Develop strategies for responsible AI development.
• Understand the societal impact of AI technologies.
Recommended Book: "Artificial Unintelligence: How Computers Misunderstand the World" by Meredith
Broussard
Publisher : Mit Pr (January 1, 2018)
Language : English
Hardcover : 237 pages
ISBN-10 : 0262038005
ISBN-13 : 978-0262038003
Research Module
9. AI 950: Research Seminar in Artificial Intelligence (6 credits)
• Course Description: Engage in original research under the guidance of faculty mentors. Present
and defend research findings in a seminar setting.
• Course Objectives:
• Conduct original research in a specialized area of AI.
• Present research findings effectively.
• Receive and provide constructive feedback in a seminar setting.
Dissertation Proposal and Research
10. AI 999: Dissertation Proposal and Research (12 credits)
• Course Description: Develop and execute an original research project in a specialized area of AI.
Regular meetings with the dissertation committee to review progress.
• Course Objectives:
• Define and refine a research problem.
• Conduct comprehensive literature reviews.
• Demonstrate proficiency in research methodology and analysis.
Required Book: “The Dissertation Journey: A Practical and Comprehensive Guide to Planning, Writing,
and Defending Your Dissertation (Updated)”
Publisher : Corwin; Third Edition (Revised Edition) (September 17, 2018)
Language : English
Paperback : 240 pages
ISBN-10 : 1506373313
ISBN-13 : 978-1506373317
Recommended Electives:
AI 910: Explainable AI and Interpretability (3 credits)
Course Description: This elective delves into the critical area of Explainable AI (XAI) and the
interpretability of machine learning models. Students explore techniques for making AI systems more
transparent, understandable, and accountable. Topics include model-agnostic interpretability methods,
visualization techniques, and the ethical implications of building interpretable AI models.
Course Objectives:
1. Understand the importance of explainability in AI.
2. Explore and apply model-agnostic interpretability methods.
3. Analyze ethical considerations in building interpretable AI models.
Recommended Books:
1. "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by
Christoph Molnar
AI 920: Quantum Computing and AI (3 credits)
Course Description: This elective introduces the intersection of Quantum Computing and Artificial
Intelligence. Students explore the principles of quantum mechanics, quantum algorithms, and their
applications in machine learning. The course covers quantum machine learning models, quantum neural
networks, and potential advancements in AI leveraging quantum computing.
Course Objectives:
1. Understand the principles of quantum computing.
2. Explore quantum algorithms and their applications in AI.
3. Investigate potential advancements and challenges in quantum-based AI.
Recommended Books:
1. "Quantum Computing for Computer Scientists" by Noson S. Yanofsky and Mirco A. Mannucci
Publisher : Cambridge University Press; Illustrated edition (August 11, 2008)
Language : English
Hardcover : 402 pages
ISBN-10 : 0521879965
ISBN-13 : 978-0521879965
AI 930: AI in Healthcare (3 credits)
Course Description: This elective focuses on the application of AI techniques in healthcare. Students
examine the role of AI in medical image analysis, predictive modeling for disease diagnosis, personalized
treatment plans, and healthcare system optimization. Ethical considerations and privacy concerns in
implementing AI in healthcare are also explored.
Course Objectives:
1. Explore AI applications in medical image analysis and diagnosis.
2. Develop predictive models for healthcare applications.
3. Understand ethical considerations and privacy concerns in AI healthcare applications.
Recommended Books:
1. Machine Learning and AI for Healthcare
Publisher : Apress; 2nd ed. edition (December 16, 2020)
Language : English
Paperback : 440 pages
ISBN-10 : 148426536X
ISBN-13 : 978-1484265369
AI 940: AI for Cybersecurity (3 credits)
Course Description: This elective explores the intersection of AI and cybersecurity, addressing the
evolving landscape of cyber threats. Students delve into the use of AI for anomaly detection, threat
intelligence, and automated response systems. The course also covers ethical considerations and
challenges in applying AI to enhance cybersecurity.
Course Objectives:
1. Analyze the role of AI in addressing cybersecurity challenges.
2. Develop skills in anomaly detection and threat intelligence using AI.
3. Investigate ethical considerations in applying AI to cybersecurity.
Recommended Books:
1. "Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your
machines intelligent using the power of machine learning" by Alessandro Parisi
Publisher : Packt Publishing (August 2, 2019)
Language : English
Paperback : 342 pages
ISBN-10 : 1789804027
ISBN-13 : 978-1789804027
