Postgraduate Certificate in Artificial Intelligence
Postgraduate Certificate in Artificial Intelligence
Downloads
Postgraduate Certificate in Artificial Intelligence
Downloads
About this degree
This certificate is designed to provide students with advanced knowledge and practical skills in the rapidly evolving field of AI. This program offers a comprehensive curriculum that covers core AI concepts, including machine learning, neural networks, natural language processing, and robotics. Students will gain a deep understanding of both theoretical and applied aspects of AI, preparing them to solve complex problems and innovate in various industries. The program emphasises hands-on experience through projects, case studies, and real-world applications, enabling students to apply AI techniques to create intelligent systems and drive decision-making processes. The program is tailored for professionals and graduates who aspire to lead in the AI domain, whether in research, development, or management roles. With a focus on flexibility and accessibility, this degree allows students to balance their studies with professional and personal commitments. Graduates will be equipped to take on advanced roles in AI, such as data scientists, AI engineers, and AI project managers, and will be well-prepared to contribute to the development and deployment of AI technologies across a wide range of sectors, including healthcare, finance, and technology.
Postgraduate Certificate in Artificial Intelligence
Downloads
What you'll learn
- Design and develop AI models using state-of-the-art tools and techniques, applying machine learning principles to solve complex problems.
- Apply AI techniques to industry-specific applications, utilising data science and computational intelligence for real-world decision-making.
- Optimise AI models and algorithms through iterative testing and refinement, improving efficiency and effectiveness in various applications.
- Execute predictive modelling using advanced data analytics and machine learning approaches, with a focus on accurate predictions and insights.
- Lead AI-focused projects, managing resources, timelines, and stakeholders to deliver AI-driven solutions that align with business goals.
Postgraduate Certificate in Artificial Intelligence
Downloads
Course Structure
About
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 earn 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.
Teachers
Intended learning outcomes
- Develop a specialised knowledge of key strategies related to JavaScript.
- Acquire knowledge of popular style guides and good coding practices to build readable and reusable code which is also highly performant.
- Develop a critical knowledge of JavaScript.
- Critically evaluate diverse scholarly views on JavaScript.
- Critically assess the relevance of theories or business applications in the domain of technology.
- Apply an in-depth domain-specific knowledge and understanding to JavaScript tools.
- Creatively apply JavaScript concepts to develop critical and original solutions for computational problems.
- Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
- Autonomously gather material and organise into coherent problem sets or presentations.
- Demonstrate self-direction in research and originality in solutions developed for JavaScript.
- Solve problems and be prepared to take leadership decisions related to the methods and principles of JavaScript.
- Apply a professional and scholarly approach to research problems pertaining to JavaScript.
- Create synthetic contextualised discussions of key issues related to JavaScript.
- Act autonomously in identifying research problems and solutions related to JavaScript.
- Efficiently manage interdisciplinary issues that arise in connection to JavaScript.
About
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.
Teachers
Intended learning outcomes
- Compare and contrast narrow AI, general AI, and superintelligent AI, and evaluate their use cases in various industries.
- Identify the foundational concepts of artificial intelligence including machine learning, neural networks, and natural language processing.
- Explain the key milestones and advancements in the field of AI, from its inception to modern-day applications.
- Utilise AI tools and frameworks for practical AI development.
- Implement and run AI algorithms, such as decision trees and k-nearest neighbours, on datasets to solve classification and regression tasks.
- Assess the accuracy, precision, recall and evaluate the performance of AI models using standard metrics.
- Evaluate the societal and ethical challenges posed by AI, such as bias, privacy concerns, and job displacement, and propose strategies to mitigate these issues.
- Work effectively in groups to design, develop, and present AI solutions, showcasing strong teamwork and communication skills.
- Create simple AI systems or prototypes that address specific real-world challenges, demonstrating an understanding of AI principles.
About
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 would also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.
Teachers
Intended learning outcomes
- Acquire knowledge of HTML5, CSS and Frameworks like Bootstrap 4.
- Critically assess the relevance of theories for business applications in the domain of technology
- Develop a specialised knowledge of key strategies related to Front end UI/UX development.
- Develop a critical knowledge of Front end UI/UX development.
- Critically evaluate diverse scholarly views on Front end UI/UX development.
- Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
- Creatively apply Front end UI/UX development applications to develop critical and original solutions for computational problems.
- Autonomously gather material and organise into a coherent problem set or presentation.
- Apply an in-depth domain-specific knowledge and understanding to technology.
- Act autonomously in identifying research problems and solutions related to Front end UI/UX development.
- Apply a professional and scholarly approach to research problems pertaining to Front end UI/UX development.
- Solve problems and be prepared to take leadership decisions related to the methods and principles of Front end UI/UX development
- Efficiently manage interdisciplinary issues that arise in connection to Front end UI/UX development.
- Create synthetic contextualised discussions of key issues related to Front end UI/UX development.
- Demonstrate self-direction in research and originality in solutions developed for Front end UI/UX development.
About
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.
Teachers
Intended learning outcomes
- Define and explain key ethical principles in AI, such as fairness, transparency, accountability, and privacy.
- Critically analyse real-world case studies of ethical failures and successes in AI, drawing lessons for future practice.
- Recognize and describe common ethical challenges and dilemmas encountered in AI development, including bias, discrimination, and data privacy issues.
- Assess AI systems for ethical compliance using established frameworks and guidelines, ensuring they align with societal values and legal requirements.
- Design and implement strategies to mitigate bias in AI models, using techniques such as re-sampling, fairness-aware algorithms, and interpretability tools.
- Perform ethical risk assessments for AI projects, identifying potential harms and proposing measures to minimise them.
- Lead and guide multidisciplinary teams in developing and implementing AI systems that adhere to ethical standards, fostering a culture of ethical AI within their organisations.
- Demonstrate the competency to advocate for ethical AI practices in industry and policy discussions, effectively communicating the importance of ethics in AI to diverse stakeholders.
- Demonstrate the ability to design AI solutions that prioritise ethical considerations, balancing innovation with responsibility to ensure positive societal impact.
About
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 e-commerce, online learning and/or photo-sharing.
Teachers
Intended learning outcomes
- Develop a critical knowledge of Back End Development.
- Acquire knowledge of key aspects of Node.js like setup, package manager, client-server programming and connecting to various databases and REST.
- Develop a specialised knowledge of key strategies related to Back End Development.
- Critically assess the relevance of theories for business applications in the domain of technology.
- Critically evaluate diverse scholarly views on Back End Development.
- Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
- Creatively apply Back End Development tools to develop critical and original solutions for computational problems.
- Autonomously gather material and organise it into coherent problem sets or presentations.
- Apply an in-depth domain-specific knowledge and understanding to Back End Development applications.
- Apply a professional and scholarly approach to research problems pertaining to Back End Development.
- Create synthetic contextualised discussions of key issues related to Back End Development.
- Efficiently manage interdisciplinary issues that arise in connection to Back End Development.
- Demonstrate self-direction in research and originality in solutions developed for Back End Development.
- Solve problems and be prepared to take leadership decisions related to the methods and principles of Back End Development.
- Act autonomously in identifying research problems and solutions related to Back End Development.
About
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.
Teachers
Intended learning outcomes
- Critically evaluate diverse scholarly views on front end development.
- Develop a specialised knowledge of key strategies related to front end development.
- Develop a critical knowledge of front end development.
- Acquire knowledge of popular frameworks/libraries in use: React.js, jQuery and AngularJS.
- Critically assess the relevance of theories for business applications in the domain of technology.
- Autonomously gather material and organise it into coherent problem sets or presentations.
- Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
- Creatively apply front end development applications.to develop critical and original solutions for computational problems.
- Apply an in-depth domain-specific knowledge and understanding to front end development solutions.
- Solve problems and be prepared to take leadership decisions related to the methods and principles of front end development.
- Efficiently manage interdisciplinary issues that arise in connection to front end development.
- Apply a professional and scholarly approach to research problems pertaining to front end development.
- Create synthetic contextualised discussions of key issues related to front end development.
- Act autonomously in identifying research problems and solutions related to front end development.
- Demonstrate self-direction in research and originality in solutions developed for front end development.
Postgraduate Certificate in Artificial Intelligence
Downloads
Apply Now
Ready to start your journey? Apply for Postgraduate Certificate in Artificial Intelligence today.