Courses

Core Courses

Mathematics for AI

This first foundational course is designed to equip students with the fundamental mathematical concepts essential for understanding and developing artificial intelligence (AI) algorithms. It aims to provide a solid foundation that students can build upon in subsequent, more specialised AI and machine learning courses.

Topics include linear algebra, calculus, probability, statistics, and discrete mathematics.

Students will learn to apply these mathematical tools to model and solve AI-related problems, such as optimisation for machine learning, understanding the geometry of data, and probabilistic reasoning for uncertain environments.

While the course assumes that the student does not have any existing mathematical knowledge, any existing knowledge on these topics would be a great plus.

Fundamentals of Machine Learning

This course introduces the fundamental concepts and algorithms of machine learning (ML), emphasising both the theoretical underpinnings and practical applications across a broad range of techniques.

Students will delve into supervised learning techniques such as regression, classification, decision trees, and support vector machines, each offering distinct approaches to modelling data.

The course also covers unsupervised learning methods including clustering algorithms like K-means and hierarchical clustering, and dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE). Further, the course also explores ensemble methods, such as random forests and gradient boosting machines, which combine multiple models to produce more powerful and reliable predictions.

An overview of optimisation algorithms critical for training these models, like gradient descent and its variants, will also be discussed. In addition to practical use case studies that involve implementing and adjusting these ML algorithms to tackle real-world data problems, students will engage in discussions about model evaluation metrics, model selection, and the ethical implications of machine learning.

This comprehensive foundation prepares students for advanced topics in artificial intelligence and data science, ensuring a well-rounded understanding of both popular and powerful machine learning methodologies beyond just neural networks.

Design Innovation

This foundational design course explores the intersection of design thinking and technology, focusing on innovative approaches to solve complex problems in various domains.

Students will learn about the principles of design thinking, including empathy, ideation, prototyping, and testing, and how these principles can be applied to create innovative solutions in tech-driven environments.

The course will cover case studies from industries such as healthcare, urban planning, and consumer electronics to illustrate how design innovation can lead to substantial improvements in products, services, and processes.

Throughout the course, students will engage in hands-on use case studies where they will apply design thinking methodologies to real-world challenges, using tools like user journey mapping, wireframing, and usability testing.

The course aims to cultivate a mindset that emphasises creativity, user-centricity, and iterative learning, all crucial for driving innovation in any tech-oriented career.

Deep Learning for Enterprise

This course serves as a pivotal bridge between foundational machine learning concepts and more advanced specialty topics in AI.

It introduces students to the core principles and applications of deep learning through hands-on experience with popular frameworks, the most popular currently being PyTorch.

Students will learn to implement, train, and fine-tune various deep learning architectures from scratch. These architectures include deep linear neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and simple transformers, applying them to real-world tasks like image recognition, natural language processing, and sequence modelling.

The curriculum emphasises practical skills in handling data, selecting appropriate layers for models and optimising neural network architectures, and using GPUs for efficient training.

Additionally, the course introduces advanced deep learning techniques such as transfer learning, generative adversarial networks (GANs), and autoencoders.

Through project-based learning, students will integrate and apply their knowledge from earlier courses in machine learning, preparing them for subsequent specialised courses on topics such as Natural Language Processing, Computer Vision, and Reinforcement Learning.

Humanistic Design: Ethics, Care and Accountability in the Age of AI

This course examines the evolving challenges and responsibilities of leadership in the era of artificial intelligence (AI). Drawing from the adage, "With great power comes great responsibility," we explore what moral responsibility entails for leaders when AI is part of the decision-making process. What do we mean by a "good" leader? Is it only a matter of efficiency, or does the "good" also involve a specific ethical capability? What obligations do leaders face, and how do their rights and responsibilities change with the integration of AI?

This course will deal with the specific ethical issues arising from a position of leadership in the era of AI, considering not just efficiency but also ethical capabilities, especially in diverse contexts like war, business, and politics. We will look at connected themes, such as authority, power, governance, and technology, to address how leaders' rights and responsibilities evolve with AI implementation and explore key ethical issues leaders face today.

Natural Language Processing and Generative AI

This course dives into the specialised field of Natural Language Processing (NLP) and explores the emerging capabilities of generative AI. Students will learn about the core techniques in NLP including text preprocessing, sentiment analysis, machine translation, and named entity recognition.

The course will also cover advanced topics such as sequence-to-sequence models, attention mechanisms, and the latest developments in transformer architectures like GPT and BERT. In addition to theoretical foundations, the course strongly emphasises practical applications of NLP and generative models in various domains, such as automated content creation, chatbots, and assistive technologies.

Students will get hands-on experience with libraries and frameworks that are pivotal in building NLP applications, such as NLTK, spaCy, and Hugging Face’s Transformers.

Projects in this course will involve students creating their own NLP models to address real-world problems, and experimenting with generative techniques to produce text, code, or artistic content.

Ethical considerations, particularly concerning the impact of generative AI on information authenticity and privacy, will also be a significant focus, preparing students to use these powerful tools responsibly in their future careers.

Elective Courses

MDAI-E electives are designed to empower students to dive deeper into courses in Robotics, Human-Centred Design and more. Students may tailor your learning journey to your interests and career goals while gaining advanced knowledge and skills in these dynamic areas.

AI for Sustainable Development Planning

Creative Artificial Intelligence
Digital Design Delivery
Towards Cabon-Neutral Architecture and Urban Design
Carbon-Neutral Architecture and Urban Design

Embodied Robotics

Mechanics & Materials
Modelling & Control
Soft Robotics
Robotics Intelligence

Analytics & AI in Operations

Machine Learning and Analytics
Optimisation for Data Science
Statistical Learning

Human-Centred Design

User Experience: Understanding Culture for Design
Information Visualisation for Human-Centred Design
Human Behaviour, Technology and Design
Management of Design and Technology

AI and Technology in Education

Technology-Enabled Education
Artificial Intelligence in Education
Instructional Design for Project-based Learning in STEM Education

Note: Electives offered may vary from term to term and is subject to change.

Professional Development Course

In addition to the core and elective courses, students are required to read at least one 3-credit Professional Development (PD) course. Some PD courses currently available include:

Professional Communication
Tools for Creative Design
Skills and Tools for Conducting Literature Review and Research
Research Integrity, Data Protection and Research Ethics

See PD course listing

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