Master of Science in Artificial Intelligence

This course helps students translate advanced mathematical/statistical/scientific concepts into code. This is a module for writing code to solve real-world problems. It introduces programming concepts (such as control structures, recursion, classes and objects) assuming no prior programming knowledge, to make this course accessible to advanced professionals from scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc. After building a strong foundation for converting scientific knowledge into programming concepts, the course advances to dive deeply into Object-Oriented Programming and its methodologies. It also covers when and how to use inbuilt-data structures like 1-Dimensional and 2-Dimensional Arrays before introducing the concepts of computational complexity to help students write optimized code using appropriate data structures and algorithmic design methods. The module can be taught to allow students to learn these concepts using a modern programming language such as Java or Python. The course offers students the ability to identify and solve computer programming problems in scientific fields

at a graduate level.

This course introduces basic probability theory , statistical methods and computational algorithms to perform mathematically rigorous data analysis. The course starts with basic foundational concepts of random variables, histograms, and various plots (PMF, PDF and CDF). Students learn various popular discrete and continuous distributions like Bernoulli, Binomial, Poisson, Gaussian, Exponential, Pareto, log-normal etc., both mathematically and from an applicative perspective. Students learn various measures like mean, median, percentiles, quantiles, variance and interquartile-range. Students learn the pros and cons of each metric and understand when and how to use them in practice. Students will learn conditional probability and Bayes theorem in the applied context of real-world problems in medicine and healthcare. The module teaches the foundations of non-parametric statistics and applies them to solve problems using computational tools. Students learn various methods to determine correlations rigorously in data. This is followed by applied and mathematical understanding of the statistics underlying control- treatment (A/B) experiments and hypothesis testing. The module engages computation tools in modern statics like Bootstrapping, Monte-Carlo methods, RANSAC etc.

Mathematics and computer science are closely related fields. Problems in computer science are often formalized and solved with mathematical methods. It is likely that many important problems currently facing computer scientists will be solved by researchers skilled in algebra, analysis, combinatorics, logic, and/or probability theory, as well as computer science. This course covers discrete mathematics for computer science and engineering. Topics may include asymptotic notation and growth of functions; permutations and combinations; counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions. Students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems. The focus of the course is real-world problems and applications often found in business and industry.

This is a comprehensive course focused on the practical implementation of machine learning techniques across various industries. This course delves into the application of supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. Students will learn how to leverage these techniques to solve real-world problems in areas such as healthcare, finance, marketing, and beyond. Emphasis is placed on understanding the entire machine learning pipeline, from data preprocessing and model selection to evaluation and deployment. Throughout the course, students will engage in hands-on projects and case studies that demonstrate the practical use of machine learning in real-world scenarios. By applying machine learning algorithms to datasets, students will gain invaluable experience in extracting insights and making data-driven decisions. Additionally, the course covers best practices for model optimization and performance tuning, ensuring students are equipped to create robust and scalable machine learning solutions. By the end of the course, students will have a solid foundation in machine learning applications, empowering them to innovate and drive progress in their respective fields.

This course is dedicated to equipping students with the skills to develop and apply models that forecast future trends and behaviours based on historical data. This course covers a range of predictive techniques, including linear and logistic regression, time series analysis, ensemble methods, and advanced machine learning algorithms. Students will learn how to build, validate, and deploy predictive models to make accurate forecasts in various domains such as finance, healthcare, and marketing.

The course emphasises both theoretical understanding and practical application, with students engaging in hands-on projects and case studies that demonstrate real-world uses of predictive modelling. By working with diverse datasets and employing state-of-the-art tools, students will gain experience in model selection, performance evaluation, and optimization. By the end of the course, students will be adept at creating robust predictive models that drive data-informed decision-making and contribute to strategic planning in their professional fields.

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, NaiveI Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

This course is dedicated to exploring the ethical, legal, and social implications of artificial intelligence technologies. This course examines key issues such as bias in AI algorithms, data privacy, transparency, accountability, and the impact of AI on employment and society. Students will engage with case studies and frameworks designed to address these challenges, learning how to develop and implement AI systems that align with ethical standards and promote fairness and inclusivity.

Through a combination of theoretical discussions and practical applications, the course equips students with the knowledge and tools necessary to navigate the complex landscape of AI ethics. Students will participate in discussions on policy, regulations, and best practices, and will work on projects that involve designing ethical AI solutions and conducting impact assessments. By the end of the course, students will be prepared to advocate for and implement ethical AI practices in their professional roles, ensuring that AI technologies are developed and used responsibly and equitably.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society. Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and critical-thinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

This course helps students translate mathematical/statistical/scientific concepts into code. This is a foundational course for writing code to solve Data Science ML & AI problems. It introduces basic programming concepts (like control structures, recursion, classes, and objects) from scratch, assuming no prerequisites, to make this course accessible to students from non-computational scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering, etc. After building a strong foundation, the course advances to dive deep into core Mathematical libraries like NumPy, SciPy, and Pandas. Students also learn when and how to use inbuilt-data structures like Lists, dicts, sets, and Tuples. The module introduces the concepts of computational complexity to help students write optimised code using appropriate data structures and algorithmic design methods. The module does not dive deep into the data structures and algorithm design methods in this course – that is available in the ‘Data Structures and Algorithms’ module. This course is valuable for all students specialising in mathematical sub-areas of CS like ML, Data Science, Scientific Computing, etc.

This course provides a strong mathematical and applicative introduction to Deep Learning. The module starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularization by dropouts, batch normalization etc., to ensure that deep MLPs can be successfully trained. The module teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing

Data is the fuel driving all major organisations. This course helps you understand how to process data at scale.

From understanding the fundamentals of distributed processing to designing data warehousing and writing ETL (Extract Transform Load) pipelines to process batch and streaming data.

Students will learn a comprehensive view of the complete Data Engineering lifecycle.

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There are now powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations.  The course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion charts, trends and forecasting, formatting, stories, performance recording and advanced mapping. At the end of this course, students will be prepared, if they desire, to earn such industry desktop certifications as a Tableau Desktop Specialist, a Tableau Certified Associate, or a Tableau Certified Professional.

This course is designed to equip students with the skills to harness AI technologies for enhanced decision-making processes. This course explores the integration of AI techniques, such as predictive analytics, decision trees, reinforcement learning, and optimization algorithms, to support and improve decision-making in various contexts. Students will learn how to develop and implement AI-driven models that can analyse complex data, predict outcomes, and provide actionable insights to inform strategic decisions in business, healthcare, finance, and other sectors.

Through a blend of theoretical knowledge and practical applications, students will engage in projects and case studies that illustrate the power of AI in transforming decision-making practices. They will gain hands-on experience with tools and methodologies used to build intelligent decision support systems, ensuring they can apply these skills to real-world challenges. By the end of the course, students will be adept at creating AI solutions that enhance decision-making capabilities, positioning themselves as valuable assets in any organisation seeking to leverage AI for competitive advantage.

This course is focused on the techniques and technologies that enable computers to understand, interpret, and generate human language. This course covers foundational topics in NLP, including tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. Students will explore advanced methods such as word embeddings, sequence-to-sequence models, and transformers, which are essential for applications like language translation, chatbots, and text summarization. Combining theoretical insights with practical implementation, the course includes hands-on projects and case studies that apply NLP techniques to real-world datasets. Students will gain experience using popular NLP libraries and frameworks, such as NLTK, SpaCy, and Hugging Face Transformers, to develop and refine language models. By the end of the course, students will be well-prepared to build and deploy sophisticated NLP solutions, enhancing their ability to work with and analyse textual data in various professional and research contexts.

This course is focused on equipping students with the skills and knowledge needed to effectively incorporate AI technologies into various business and industrial processes. This course explores a wide range of strategies for integrating AI, including deployment methodologies, system interoperability, and change management. Students will learn about the challenges and best practices for implementing AI solutions in real-world environments, ensuring seamless integration with existing systems and maximising organisational benefits. Throughout the course, students will engage in case studies, practical projects, and interactive discussions that highlight successful AI integration across different sectors. By understanding the strategic, technical, and ethical considerations involved in AI deployment, students will be prepared to lead AI initiatives and drive innovation in their organisations. This course is essential for aspiring AI professionals aiming to bridge the gap between cutting-edge AI technologies and their practical applications, ultimately contributing to the advancement of the AI field.

This course is dedicated to mastering the art and science of designing effective prompts for large language models (LLMs). This course explores the principles of crafting precise and impactful prompts that elicit desired responses from advanced AI models such as GPT-3, BERT, and their successors. Students will learn various strategies for prompt construction, including techniques for optimising clarity, context, and specificity to enhance model performance in tasks like text generation, translation, and summarization. Through a combination of theoretical insights and practical exercises, students will gain hands-on experience in developing and refining prompts to solve real-world problems. The course includes case studies and projects that demonstrate how prompt engineering can be applied across different domains, such as customer service, content creation, and data analysis. By the end of the course, students will be adept at leveraging prompt engineering to harness the full potential of large language models, enabling them to drive innovation and efficiency in their professional endeavours with AI.

This is a project-based course, with the aim of building the required skills for creating web-based software systems. The course covers the entire lifecycle of building software projects, from requirement gathering and scope definition from a product document, to designing the architecture of the system, and all the way to delivery and maintenance of the software system. The course covers both frontend, which is, building browser-based interfaces for users, using frontend web frameworks, and also building the backend, which is the server running an API to serve the information to the frontend, and running on an

SQL or similar database management system for storage.

All aspects of delivering a software project, including security, user authentication and authorisation, monitoring and analytics, and maintaining the project are covered. The course also covers the aspects of project maintenance, like using a version control system, setting up continuous integration and deployment pipelines

and bug trackers. The Applied Computer Science Project will focus on the outcome of how computer science and its associated tools as a field of study can be beneficial to other fields of study or how such tools can be applied to modify existing processes for better outcomes.