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 optimised code using appropriate data structures and algorithmic design methods.

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 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 aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

This course provides a strong mathematical and applicative introduction to Deep Learning. The course 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, regularisation by dropouts, batch normalisation etc., to ensure that deep MLPs can be successfully trained.

The course 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.

This course is designed to bridge the gap between data theory and real-world applications. This course focuses on the end-to-end process of data analytics, including data collection, cleaning, exploratory data analysis, and visualisation. Students will learn how to apply statistical methods and machine learning techniques to analyse and interpret complex datasets, uncovering actionable insights that drive strategic decision-making across various domains such as business, healthcare, and technology.

The course combines theoretical instruction with hands-on projects, allowing students to work with real datasets and employ state-of-the-art tools and software. By engaging in case studies and practical exercises, students will develop the skills necessary to tackle data-driven problems and present their findings effectively. Upon completion, students will be well-equipped to leverage data analytics to solve real-world challenges and contribute to data-informed decision-making processes in their professional careers.

This course is designed to bridge the gap between theoretical AI concepts and their real-world applications across various industries. This course explores how AI technologies are implemented to solve industry-specific challenges and drive innovation in fields such as healthcare, finance, manufacturing, and retail. Students will examine case studies and practical examples of AI solutions that optimise processes, enhance decision-making, and create value for businesses and organisations.

Through hands-on projects and collaborative assignments, students will gain experience in deploying AI systems and tools tailored to industry needs. They will work with real-world datasets and use industry-standard platforms to develop and implement AI solutions, learning to address unique operational and strategic problems. By the end of the course, students will be equipped with the skills and insights needed to apply AI technologies effectively in various industrial contexts, making them valuable assets in transforming and advancing industry practices through artificial intelligence.

This is a course that focuses both on architectural design and practical hands-on learning of the most used cloud services. The module extensively uses Amazon Web services (AWS) to show real world code examples of various cloud services. It also covers the core concepts and architectures in a platform agnostic manner so that students can easily translate these learnings to other cloud platforms (like Azure, GCP etc.). The course starts with virtualization and how virtualized compute instances are created and configured. Students also learn how to auto-scale applications using load balancers and build fault tolerant applications across a geographically distributed cloud. As relational databases are widely used in most enterprises, students learn how to migrate and scale (both vertically and horizontally) these databases on the cloud while ensuring enterprise grade security. Virtual private clouds enable us to create a logically isolated virtual network of computer resources. Students learn to set up a VPC using virtualized-compute-servers on AWS. The course also covers the basics of networking while setting up a VPC. Students learn of the architecture and practical aspects of distributed object storage and how it enables low latency and high availability data storage on the cloud.

This course is a hands-on course covering JavaScript from basics to advanced concepts in detail using multiple examples. We start with basic programming concepts like variables, control statements, loops, classes and objects. Students also learn basic data-structures like Strings, Arrays and dates. Students also learn to debug our code and handle errors gracefully in code. We learn popular style guides and good coding practices to build readable and reusable code which is also highly performant. We then learn how web browsers execute JavaScript code using V8 engine as an example. We also cover concepts like JIT-compiling which helps JS code to run faster. This is followed by slightly advanced concepts like DOM, Async-functions, Web APIs and AJAX which are very popularly used in modern front end development. We learn how to optimise JavaScript code to run on both mobile apps and mobile browsers along with Desktop browsers and as desktop apps via ElectronJS. Most of this course would be covered via real world examples and by learning from JS code of popular open-source websites and libraries.

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.

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. Students will apply AI techniques—such as machine learning models, neural networks, computer vision, or NLP—to solve a real-world computational problem. The project should demonstrate an understanding of AI theory and its application to practical scenarios.

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 is a hands-on course on designing responsive, modern, and lightweight UI for

web, mobile, and desktop applications using HTML5, CSS, and Frameworks like

Bootstrap 4. This course starts with an introduction to how web browsers, mobile

apps, and web servers work. We then dive into each of the nitty-gritty details of

HTML5 to build webpages. We would start with simple web pages and then

graduate to more complex layouts and features in HTML like forms, iFrames,

multimedia playback, and using web APIs. We then go on to learn stylesheets based on CSS 4 and how browsers interpret CSS files to render web pages. Once again, we use multiple real-world example web pages to learn the internals of CSS4. We learn popular good practices for writing responsive HTML and CSS code, which is also interoperable on mobile browsers, apps, and desktop apps. We would introduce students to building desktop apps using HTML and CSS using toolkits like Electron. We also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

preadsheets for Data Understanding introduces students to the principles and techniques of data cleaning, handling data sets of varying sizes, and visualising data/data storytelling. Students will also learn the basics of predictive modelling from data sets. These are all introduced through the means of Microsoft Excel, the industry-standard spreadsheet program. Students will learn how to use inbuilt functions, as well as techniques such as creating and modifying pivot tables.

This is a core and foundational course which aims to equip the student with the ability to model, design, implement and query relational database systems for real-world data storage & processing needs. Students would start with diagrammatic tools (ER-diagram) to map a real world data storage problem into entities, relationships and keys. Then, they learn to translate the ER-diagram into a relational model with tables. SQL is then introduced as a de facto tool to create, modify, append, delete, query and manipulate data in a relational database. Due to SQL’s popularity, the course spends considerable time building the ability to write optimised and complex queries for various data manipulation tasks. The module exposes students to various real world SQL examples to build solid practical knowledge. Students then move on to understanding various trade-offs in modern relational databases like the ones between storage space and latency. Designing a database would need a solid understanding of normal forms to minimise data duplication, indexing for speedup and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples from various domains. Most of this course uses the open source MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.

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 builds on foundational AWS knowledge, diving deeper into the platform's sophisticated features and services. Students will explore advanced networking configurations, security and compliance measures, and serverless architectures. Emphasis will be placed on practical applications, allowing students to design and implement complex AWS architectures, automate infrastructure management with Infrastructure as Code (IaC) tools, and optimize costs for scalable solutions.In addition to technical skills, the course covers advanced topics in containerization, orchestration with AWS services, and the development of continuous integration/continuous deployment (CI/CD) pipelines. Students will gain hands-on experience through labs and projects that simulate real-world scenarios, ensuring they can effectively deploy, manage, and scale applications on AWS. By the end of the course, students will be proficient in leveraging AWS's full potential to meet specific business requirements, ensuring security, compliance, and cost-efficiency in cloud environments.

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are 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. 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 chart, 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 equips learners with the essential skills to navigate and manage Linux servers effectively. The course begins by demystifying the Linux command line interface, learning the fundamentals students need to interact with the system. As learners progress, they will explore powerful command-line utilities like grep, sed, awk, and more, allowing them to manipulate and analyse data efficiently.Next, they will delve into the Linux file system structure, gaining a solid understanding of user permissions and access control. This knowledge is crucial for ensuring secure and organised server environments.Finally, the course empowers students to write their own shell scripts. They will learn how to craft conditional statements (if/else) and loops (for loops) to automate repetitive tasks and streamline the workflow. By the end, they will be able to write scripts to solve real-world problems and significantly boost their Linux server administration skills.

This core foundational course equips students with knowledge of Database Management Systems (DBMS) and Computer Networks.The course starts with Entity-Relationship (ER) diagrams, a visual tool for mapping real-world data storage problems.Students learn to translate ER diagrams into a relational model with tables. SQL, the standard language for relational databases, is then introduced. Students will spend significant time building proficiency in writing optimised and complex SQL queries for various data manipulation tasks. Real-world examples will be used to solidify practical knowledge.

Next, the course explores trade-offs in modern relational databases, such as storage space versus latency. Designing efficient databases requires understanding normal forms to minimise data duplication, indexing for speed improvements,and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples.The course utilises open-source MySQL databases and cloud-hosted relational databases (like Amazon RDS) for assignments, allowing students to apply learned concepts on real databases.Following the DBMS section, the course transitions to Computer Networks. Here, students will delve into foundational concepts like the OSI model, TCP/IP model, TCP/UDP protocols, subnetting, DNS (Domain Name System), Network Address Translation (NAT), private networks, Secure Sockets Layer (SSL), and network security principles.

This course is aimed at deepening students' understanding of cutting-edge topics in artificial intelligence. This course delves into advanced methodologies such as generative adversarial networks (GANs), meta-learning, and advanced reinforcement learning techniques. Students will explore the theoretical underpinnings and practical implementations of these sophisticated AI concepts, focusing on their applications in complex problem-solving and innovation across various domains. Through a blend of advanced theoretical discussions and hands-on projects, students will engage with state-of-the-art tools and techniques, working on real-world problems and research projects. The course encourages critical thinking and problem-solving, preparing students to tackle the challenges of implementing and advancing AI technologies. By the end of the course, students will have a robust understanding of advanced AI concepts and be well-equipped to contribute to cutting-edge research and development in the field of artificial intelligence.

DevOps Tools Part 2 is an advanced course designed for students building on the foundational knowledge from DevOps Tools Part 1. his course delves deeper into advanced DevOps tools and techniques that drive modern software development and operations. Students will explore sophisticated CI/CD pipelines with tools like GitLab CI/CD and Azure DevOps, and master infrastructure as code (IaC) with Terraform and AWS CloudFormation. The course emphasizes practical, hands-on experience, enabling students to automate and manage complex cloud environments effectively.In addition to advanced CI/CD and IaC, students will learn about service mesh architectures with Istio, advanced container orchestration with Kubernetes, and continuous monitoring and observability with tools such as Grafana and Jaeger. The course also covers security practices in DevOps, including integrating security tools into the CI/CD pipeline and managing secrets with HashiCorp Vault. By the end of the course, students will have the expertise to design, implement, and manage scalable, secure, and efficient DevOps workflows, preparing them for leadership roles in the field.

This course will focus on the principles and techniques involved in training large language models (LLMs). This course provides an in-depth understanding of the architectures and training methodologies used to develop powerful language models like GPT-3, BERT, and their successors. Students will explore the complexities of model training, including data preprocessing, tokenization, model architecture design, and fine-tuning. Emphasis will be placed on understanding the computational resources and optimization strategies required to train LLMs effectively. Throughout the course, students will engage in hands-on projects that involve training and fine-tuning LLMs on diverse datasets, gaining practical experience with tools and frameworks such as TensorFlow and PyTorch. Case studies will illustrate the application of LLMs in natural language processing tasks, such as text generation, translation, summarization, and question answering. By the end of the course, students will be equipped with the knowledge and skills to train large language models, enabling them to contribute to the development of state-of-the-art AI systems that leverage advanced language understanding and generation capabilities

his course is focused on the advanced techniques and architectures used to build sophisticated AI systems. This course provides an in-depth exploration of neural networks, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep learning models. Students will gain a thorough understanding of how these models are designed, trained, and optimised to tackle complex tasks such as image recognition, natural language processing, and predictive analytics. Through a combination of theoretical concepts and practical implementations, students will engage with cutting-edge tools and frameworks, such as TensorFlow and PyTorch, to develop and experiment with deep learning models. The course includes hands-on projects and case studies that highlight the application of neural networks in real-world scenarios, enabling students to build and fine-tune models for diverse applications. By the end of the course, students will be proficient in designing and deploying advanced neural network architectures, positioning themselves at the forefront of AI technology and innovation.

This course is designed to provide students with a comprehensive understanding of the principles and practices of robotics and automated systems. This course covers key topics such as robot kinematics, dynamics, control systems, and sensor integration. Students will explore the development and deployment of robotic systems for various applications, including manufacturing, logistics, and autonomous vehicles, gaining insights into the design, programming, and operation of robots.

Combining theoretical concepts with practical experience, the course features hands-on projects and case studies that illustrate the real-world implementation of robotics and automation technologies. Students will work with simulation tools and robotic platforms to design, test, and refine robotic systems, developing the skills necessary to address complex automation challenges. By the end of the course, students will be adept at integrating robotics and automation into various industries, driving innovation and efficiency through advanced technology solutions.

This course is focused on exploring the intersection of artificial intelligence and human cognitive processes. This course provides an in-depth look at how cognitive computing systems mimic human thought processes, including perception, reasoning, learning, and problem-solving. Students will study the underlying principles of cognitive models, natural language processing, and machine learning techniques used to develop systems that can understand, reason, and interact in ways that approximate human cognition.

Through a combination of theoretical frameworks and practical applications, students will engage with cutting-edge tools and technologies used in cognitive computing, such as neural networks and advanced data analytics. The course includes hands-on projects and case studies that demonstrate how cognitive systems are applied in areas such as personalised recommendations, intelligent assistants, and decision support systems. By the end of the course, students will have a deep understanding of cognitive computing principles and be prepared to develop sophisticated AI solutions that enhance human-computer interactions and decision-making processes.

This course is ​​aimed at equipping students with the expertise to efficiently manage and analyse large datasets using advanced AI techniques. This course covers the essential principles and methods of data processing, including data cleaning, integration, transformation, and real-time processing. Students will explore the application of machine learning algorithms, data mining, and big data technologies to extract meaningful patterns and insights from complex datasets, enabling informed decision-making and strategic planning in various industries.

Throughout the course, students will engage in hands-on projects and case studies that highlight the practical applications of intelligent data processing in domains such as healthcare, finance, marketing, and more. By utilising cutting-edge tools and platforms, students will develop the skills necessary to design and implement robust data processing pipelines that can handle the volume, variety, and velocity of modern data streams. Upon completion of the course, students will be proficient in transforming raw data into valuable intelligence, positioning themselves to drive innovation and efficiency in their respective fields through intelligent data solutions.

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 designed to introduce students to the core concepts and methodologies of data science. This course covers a broad range of topics, including data collection, cleaning, and preprocessing, as well as statistical analysis, data visualisation, and exploratory data analysis. Students will learn how to apply various data science techniques to extract valuable insights from large datasets, empowering them to make data-driven decisions in diverse fields such as business, healthcare, and technology. Throughout the course, students will engage in practical exercises and projects that emphasise the application of data science principles to real-world problems. By working with actual datasets and using state-of-the-art tools and software, students will develop the skills necessary to analyse, interpret, and present data effectively. Upon completion of the course, students will have a strong foundation in data science, enabling them to leverage data to solve complex problems and drive innovation in their professional careers within the realm of artificial intelligence.

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 provides a comprehensive overview of Amazon Web Services (AWS), focusing on core services and best practices for building and managing cloud-based infrastructure. Students will learn the fundamentals of cloud computing, explore key AWS services such as EC2, S3, RDS, and VPC, and gain hands-on experience in deploying and managing applications on the AWS platform. The course emphasizes practical skills, enabling students to design scalable, secure, and cost-effective cloud solutions.In addition to foundational AWS services, the course covers essential topics such as identity and access management (IAM), networking and security configurations, and monitoring and logging with CloudWatch. Students will also be introduced to Infrastructure as Code (IaC) using AWS CloudFormation and gain insights into setting up continuous integration/continuous deployment (CI/CD) pipelines with AWS tools. By the end of the course, students will have a solid understanding of AWS basics, equipping them with the knowledge and skills to effectively utilize AWS services in their DevOps practices and prepare for more advanced AWS coursework.

This course gives the detailed overview on how to approach Low Level Design problems with real-world case studies discussed such as Designing a Pen (Mac/Windows), TicTacToe, BookMyShow (most used event booking app, manages millions of users), Email campaign Management System and detailed design of Splitwise.

This course builds upon the introductory JavaScript course to acquaint students of popular and modern frameworks to build the front end. We focus on three very popular frameworks/libraries in use: React.js, jQuery and AngularJS. We start with React.js, one of the most popular and advanced ones amongst the three. students learn various components and data flow to learn to architect real world front end using React.js. This would be achieved via multiple code examples and code-walkthroughs from scratch. We would also dive into React Native which is a cross platform Framework to build native mobile and smart-TV apps using JavaScript. This helps students to build applications for various platforms using only JavaScript. jQuery is one of the oldest and most widely used JavaScript libraries, which students cover in detail. Students specifically focus on how jQuery can simplify event handling, AJAX, HTML DOM tree manipulation and create CSS animations. We also provide a hands-on introduction to AngularJS to architect model-view-controller (MVC) based dynamic web pages.

This course teaches students how to analyse the ways users engage with a service. This method, called product analytics, helps businesses track and analyse user data. Students will learn more deeply what is required to move a product from idea to implementation, through to launch, and then on to iterative improvements. The course teaches how to measure progress, validate or update product hypotheses, and present product learnings. Also, students will gain experience in making informed decisions, as well as how to present findings and make an analytics-informed business case to win support for a product

This course is designed to provide students with a deep understanding of the mathematical and theoretical foundations of computation. The course covers various computational models including finite automata, Turing machines, and formal grammars, which form the basis for understanding the limits and capabilities of different computational systems. Students will explore how these models apply to real-world problems and their significance in the development of advanced AI algorithms.

Through a blend of theoretical lectures and practical exercises, students will gain proficiency in constructing and analysing computational models, understanding their applications in solving complex problems, and appreciating their relevance in AI research. By the end of the course, students will be equipped with the skills to critically evaluate different computational frameworks and apply them to innovate and enhance AI systems. This course is essential for those looking to deepen their knowledge in the field of artificial intelligence and pursue careers in research, development, and advanced technological applications.

This course is designed to cultivate a deep understanding of algorithm design and analysis. This course focuses on the principles of algorithmic problem-solving, teaching students how to develop efficient and effective algorithms for a wide range of computational problems. Key topics include algorithmic strategies such as divide-and-conquer, dynamic programming, greedy algorithms, and graph algorithms, along with an emphasis on computational complexity and optimization techniques.

Through a blend of theoretical instruction and practical exercises, students will learn to approach problems systematically and devise algorithms that are both correct and optimised for performance. The course includes hands-on projects that challenge students to apply algorithmic thinking to real-world AI problems, enhancing their analytical and coding skills. By the end of the course, students will be equipped with the skills to design, implement, and evaluate algorithms, preparing them for advanced AI coursework and professional roles that require robust problem-solving abilities.

This course is aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

This is a foundational course on building server-side (or backend) applications using popular JavaScript runtime environments like Node.js. Students will learn event driven programming for building scalable backend for web applications. The module teaches various aspects of Node.js like setup, package manager, client- server programming and connecting to various databases and REST APIs. Most of these concepts would be covered in a hands-on manner with real world examples and applications built from scratch using Node.js on Linux servers. This course also provides an introduction to Linux server administration and scripting with special focus on web-development and networking. Students learn to use Linux monitoring tools (like Monit) to track the health of the servers. The module also provides an introduction to Express.js which is a popular light-weight framework for Node.js applications. Given the practical nature of this course, this would involve building actual website backends via assignments/projects for ecommerce, online learning and/or photo-sharing.

This course provides a strong mathematical and applicative introduction to Deep Learning. The course 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, regularisation by dropouts, batch normalisation etc., to ensure that deep MLPs can be successfully trained.

The course 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.

The ability to solve problems is a skill, and just like any other skill, the more one practices, the better one gets. So how exactly does one practice problem solving? Learning about different problem-solving strategies and when to use them will give a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. Building a toolbox of problem-solving strategies will improve problem solving skills. With practice, students will be able to recognize and choose among multiple strategies to find the most appropriate one to solve complex problems. The course will focus on developing problem-solving strategies such as abstraction, modularity, recursion, iteration, bisection, and exhaustive enumeration. The course will also introduce arrays and some of their real-world applications, such as prefix sum, carry forward, subarrays, and 2-dimensional matrices. Examples will include industry-relevant problems and dive deeply into building their solutions with various approaches, recognizing each’s limitations (i.e when to use a data structure and when not to use a data structure). By the end of this course a student can come up with the best strategy which can optimize both time and space complexities by choosing the best data structure suitable for a given problem

The course equips students with the essential skills and knowledge to effectively lead and manage AI-focused projects. This course delves into the core principles of project management, including planning, execution, monitoring, and closing projects, with a special emphasis on methodologies that are critical in the fast-paced AI industry. Students will explore both traditional and agile project management approaches, learning to navigate the complexities of AI projects that involve interdisciplinary teams, emerging technologies, and innovative solutions.

Throughout the course, students will engage in real-world case studies and hands-on projects to develop practical skills in resource allocation, risk management, communication, and stakeholder engagement. By the end of the course, students will be adept at managing AI projects from conception to completion, ensuring that they deliver value while meeting time, cost, and quality objectives. This course prepares students to take on leadership roles in AI-driven initiatives, positioning them for success in a rapidly evolving field.

This fundamental course aims to equip students with knowledge of core Linux operating system concepts. The module will cover essential operating system functionalities, including process management, process synchronisation (concurrency), memory management techniques, and disk scheduling.In process management, students will explore how Linux manages processes throughout their lifecycle, including starting, pausing, resuming, and allocating resources. The concept of concurrency will be introduced, covering multithreading and multiprocessing. Students will also learn how asynchronous processing facilitates concurrent execution.Memory management is another key topic. The course will delve into how the operating system manages memory on both RAM and hard disk, along with concepts like virtual memory, memory pages, and page caching.

This course is designed to immerse students in the latest advancements and trends in AI. This course covers cutting-edge technologies such as deep learning, neural networks, natural language processing, computer vision, and reinforcement learning. Students will explore the innovative applications of these technologies in various domains, including healthcare, finance, robotics, and autonomous systems. The course emphasises not only understanding these technologies but also critically evaluating their potential and limitations.

Through a combination of theoretical insights and hands-on projects, students will gain practical experience with state-of-the-art AI tools and platforms. They will engage in experiments, case studies, and research activities that foster a deep appreciation of the current landscape and future directions of AI technology. By the end of the course, students will be well-equipped to contribute to the development and implementation of emerging AI solutions, positioning themselves at the forefront of technological innovation and advancement in the field of artificial intelligence.

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 aimed at providing students with an in-depth understanding of the techniques and algorithms that enable intelligent behaviour in computational systems. This course covers a wide array of topics, including neural networks, fuzzy logic, evolutionary computation, and swarm intelligence. Students will explore how these methods can be applied to solve complex problems in optimization, pattern recognition, and adaptive systems, emphasising both theoretical foundations and practical implementations.

Through a combination of lectures, hands-on projects, and case studies, students will gain experience in designing and applying computational intelligence algorithms to real-world scenarios. They will learn to develop adaptive systems that can perform tasks such as classification, prediction, and decision-making with high accuracy and efficiency. By the end of the course, students will be proficient in using computational intelligence techniques to create innovative AI solutions, positioning themselves for advanced roles in research and industry where intelligent systems are pivotal.

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.

DevOps Tools Part 1 is a comprehensive course designed for students pursuing a Master of Science in Computer Science with a specialisation in DevOps. This course introduces the essential tools and methodologies that form the backbone of modern DevOps practices. Students will gain a solid foundation in version control with Git, continuous integration/continuous deployment (CI/CD) pipelines using Jenkins, and configuration management with Ansible.

The course emphasises hands-on learning, enabling students to set up, configure, and utilise these tools in real-world scenarios, ensuring they can effectively collaborate, automate workflows, and streamline the development process.In addition to core tools, the course covers containerization with Docker and orchestration with Kubernetes, providing students with the skills to deploy and manage applications in a microservices architecture. Students will also explore monitoring and logging solutions such as Prometheus and ELK Stack to maintain system reliability and performance. By the end of the course, students will be proficient in employing a wide range of DevOps tools, laying a strong foundation for advanced DevOps practices and tools covered in subsequent courses.