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MAIE

AI and Entrepreneurship

MAIE 5101
Programming for Artificial Intelligence
[3-0-0:3]
Exclusion(s)
ARIN 5101
Description
This course covers advanced programming skills for artificial intelligence (AI) applications. Students will learn to use popular AI libraries such as NumPy, Matplotlib, and Pandas, as well as deep learning frameworks like TensorFlow and PyTorch. Topics covered include data manipulation, visualization, and building neural networks. Prior experience in object-oriented programming is required.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop advanced 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.
MAIE 5102
AI Fundamentals: Concepts and Methods
[3-0-0:3]
Co-list with
ARIN 5102
Exclusion(s)
ARIN 5102, CSIT 5900, MSBD 5015
Description
This course offers an in-depth exploration of both the theoretical and practical foundations of artificial intelligence. Students will gain an understanding of fundamental concepts and methods in key AI subfields such as search algorithms, logic, knowledge representation, machine learning, natural language processing, computer vision, robotics, sequential decision-making, and probabilistic reasoning. The course aims to equip students with the necessary knowledge to pursue more advanced AI courses.
Intended Learning Outcomes
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.
MAIE 5103
Artificial Intelligence Ethics
[3-0-0:3]
Exclusion(s)
ARIN 5104
Description
This course delves into the ethical considerations involved in designing, developing, and deploying artificial intelligence (AI) systems. Students will investigate the societal impacts of AI and the ethical dilemmas that emerge in areas such as privacy, bias, transparency, and accountability. Through case studies and discussions, students will build a framework for ethical decision-making in the development and implementation of AI systems.
Intended Learning Outcomes
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.
MAIE 5209
Design and Analysis of Algorithms
[3-0-0:3]
Description
This course presents the fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their running times. General topics include mathematical analysis of algorithms (summations and recurrences), advanced data structures (balanced search trees), algorithm design techniques (divide-and-conquer, dynamic programming, and greedy algorithms), graph algorithms (breadth-first and depth-first search, minimum spanning trees, shortest paths).
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe fundamental concepts and techniques for determining the asymptotic behavior of real-valued functions defined in natural numbers.
- 2.
  Explain recurrence equations and solve common recurrences using a variety of techniques.
- 3.
  Analyze an algorithm described in plain language or some form of pseudocode in terms of its time (or space) efficiency as a function of the size of a problem instance.
- 4.
  Explain how various data structures, including trees, heaps and disjoint set structures, influence the time efficiency of algorithms.
- 5.
  Apply general algorithmic design and analysis techniques to solving problems, including greedy, divide-andconquer and dynamic programming.
- 6.
  Identify randomization in algorithms and analyze basic randomized algorithms such as randomized quicksort and selection.
MAIE 5211
Theory of Computation
[3-0-0:3]
Description
This course is an introduction to the foundation of computation, and aims at answering some of the most fundamental questions in computer science: What is an algorithm? What can and cannot be computed at all? What can and cannot be computed efficiently? The topics covered include set theory and countability, formal languages, finite automata and regular languages, pushdown automata and context-free languages, Turing machines, undecidability, P and NP, NP-completeness.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop advanced programming skills, including the ability to manipulate and analyze large datasets, visualize data, and build and train neural network models.
- 2.
  Prove the equivalence of DFA, NFA, regular expressions and the equivalence of PDA and CFG.
- 3.
  Design finite automata and write regular expressions for regular languages.
- 4.
  Write context-free grammar (CFG) for context-free languages, especially for expressions occurring in programming languages.
- 5.
  Design pushdown automata for context-free languages, and given a context-free grammar, construct a PDA that accepts the language generated by the given grammar.
MAIE 5212
Machine Learning
[3-0-0:3]
Co-list with
ARIN 5103
Exclusion(s)
ARIN 5103, COMP 5212, CSIT 5910, MSBD 5012
Description
This course offers an extensive overview of traditional and contemporary machine learning paradigms and computational techniques. Learners will delve into the theoretical underpinnings of machine learning and acquire practical skills through engaging in software development projects. The curriculum encompasses both supervised and unsupervised learning methodologies to address a diverse array of machine learning challenges. Additionally, students will be trained in the assessment and selection of suitable machine learning models and will be equipped to implement these strategies to tackle real-world issues.
Intended Learning Outcomes
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.
MAIE 5221
Natural Language Processing
[3-0-0:3]
Co-list with
ARIN 5202
Exclusion(s)
ARIN 5202, COMP 5221, MSBD 5018
Background
Prior knowledge of Python programming and machine learning
Description
This course is designed to offer a comprehensive grasp of machine learning strategies tailored for natural language processing, along with their utilization in artificial intelligence contexts. Learners will be equipped to construct and refine an array of machine learning models to address a variety of natural language processing objectives, including sentiment analysis, text categorization, and linguistic translation. A foundational understanding of Python programming and machine learning principles is a prerequisite for this course.
Intended Learning Outcomes
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.
MAIE 5421
Computer Vision
[3-0-0:3]
Co-list with
ARIN 5201
Exclusion(s)
ARIN 5201, COMP 5421, MSBD 5016
Background
Prior knowledge of Python programming and machine learning
Description
This course offers a thorough comprehension of the machine learning methodologies utilized in computer vision, along with their relevance in artificial intelligence contexts. Participants will be trained in the construction and education of various machine learning models tailored for diverse computer vision objectives, such as image segmentation, object recognition, and the creation of visual content. A prerequisite for this course is familiarity with Python programming and foundational knowledge in machine learning.
Intended Learning Outcomes
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.
MAIE 5531
Generative Al and Large Language Models
[3-0-0:3]
Exclusion(s)
ARIN 5203
Background
Prior knowledge of Python programming and machine learning
Description
This course aims to explore the fast-evolving technologies behind generative Al and large language models. Students will gain a deep understanding of the core concepts and techniques for training these models, as well as practical skills in using them effectively through prompting and integration into broader AI systems. Prior experience in Python programming and machine learning is necessary.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the fundamental concepts of generative Al and large language models.
- 2.
  Interact effectively with large language and multimodal models through prompting for a wide range of tasks in real-world applications.
- 3.
  Integrate generative Al into AI application development.
- 4.
  Apply critical thinking and problem-solving skills to design and implement generative AI systems that meet specific requirements and constraints.
MAIE 5532
Machine Learning System
[3-0-0:3]
Description
This course offers a comprehensive exploration of the principles, methodologies, and technologies that form the backbone of machine learning systems. It covers essential aspects of building and optimizing these systems, with a balanced emphasis on both theoretical foundations and practical implementations. Throughout the course, we will delve into the design principles of these systems and explore the challenges and opportunities in creating future machine learning systems for next-generation applications and hardware platforms.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop a deep understanding of machine learning system.
- 2.
  Design scalable and efficient systems for training and deploying machine learning models.
- 3.
  Implement machine learning systems using appropriate programming languages and frameworks.
- 4.
  Optimize computational resources and algorithms to improve the performance of machine learning systems.
- 5.
  Evaluate the effectiveness and efficiency of machine learning systems using relevant metrics and benchmarks.
MAIE 5533
Artificial Intelligence Security and Privacy
[3-0-0:3]
Background
Prior knowledge of programming and artificial intelligence
Description
This course introduces potential security and privacy vulnerabilities in Artificial Intelligence (AI) and covers basic and advanced protections. Topics include security and privacy risks in AI technologies, the goal of C.I.A. (Confidentiality, Integrity and Availability) in AI technologies, basic and advanced cryptography, protocol designs for AI security and privacy, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate a comprehension of security and privacy risks in AI technology.
- 2.
  Demonstrate a comprehension of data confidentiality protection technologies.
- 3.
  Demonstrate a comprehension of data integrity protection technologies.
- 4.
  Demonstrate a comprehension of availability ensuring technologies.
- 5.
  Recognize the limitations of current methods and make improvements.
MAIE 5534
Entrepreneurial Me
[3-0-0:3]
Exclusion(s)
COMP 5911
Background
Prior knowledge of artificial intelligence
Description
This course equips aspiring AI entrepreneurs with the essential skills to launch and sustain successful ventures. The course covers entrepreneurial mindset, business planning and development, technology as competitive advantages, financing and investment, and navigating operational, legal and other challenges, complemented by real-world case studies from Hong Kong and beyond. Additionally, students will develop exit strategies to ensure business viability.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Clearly formulate, articulate, and pitch a technology based startup business.
- 2.
  Identify the basic principles and practices involved in either starting a company or planning to work in an AI start-up company, and any pitfalls to be avoided.
- 3.
  Summarize the basic principles of AI entrepreneurship.
- 4.
  Recognize and act upon opportunities for building a personal business network.
- 5.
  Evaluate potential business opportunities and develop appropriate decision-making skills so as to take best advantage of those opportunities.
MAIE 5535
Startup Seminars for AI
[1-2-0:3]
Background
Prior knowledge of artificial intelligence
Description
This hands-on course explores the real-world challenges of launching an AI-driven startup through detailed case studies of both successful and failed ventures. Students will gain valuable insights into the key factors that determine startup outcomes. Interactive workshops will focus on Shanghai, covering local regulations, essential agencies and organizations. The curriculum combines open-format lectures with practical sessions to provide both theoretical knowledge and actionable skills. Graded P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Analyze an AI startup business to identify potential risks and opportunities.
- 2.
  Identify unique/novel elements of an idea/product based on patent searches.
- 3.
  Register an AI company.
- 4.
  Execute operational details related to setting up a start-up, including banking, personnel, accounting and tax.
- 5.
  Articulate the various government and non-government support structures in Shanghai area for a given area-focused startup.
MAIE 6000
Special Topics
[3-0-0:3]
Description
This course covers selected topics in artificial intelligence and entrepreneurship that reflect recent developments not covered by existing MSc(AIE) courses. The course may be repeated for credit if different topics are covered.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Recognize the technical and business problems in a specific topic in artificial intelligence.
- 2.
  Identify key technical and business solutions to the problems in the topic.
- 3.
  Solve specific problems in the topic with responsible technical and business innovations.
MAIE 6500
Innovation Management for AI
[1 credit]
Description
This course aims to equip students with a comprehensive understanding of AI technology innovation management, leadership, and entrepreneurship. It delves into the intricacies of how AI technology innovation processes function, the strategies to effectively lead and manage these processes, and the methods to cultivate an environment that fosters and rewards innovation and entrepreneurial endeavors. Through a combination of seminars, interactive workshops and company visits, students will gain the essential knowledge and skills needed to drive AI innovation and entrepreneurial success in various organizational contexts. Graded PP, P or F.
MAIE 6600
Internship and Entrepreneurship for AI
[5 credits]
Background
Students are advised to commence this course after completing all required core courses in the MSc(AIE) program.
Description
Students will acquire and practice the project planning and reporting skills essential for completing a group project on a specific artificial intelligence (AI) topic relevant to the MSc(AIE) program. Students are required to complete a final report with oral examination on the final output from the internship or entrepreneurship project and whether the student indeed knows how to apply AI techniques to concrete applications. Graded PP, P or F.
Intended Learning Outcomes
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.

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