Postgraduate Diploma in Artificial Intelligence
Postgraduate Diploma in Artificial Intelligence
Downloads
Postgraduate Diploma in Artificial Intelligence
Downloads
About this degree
This Diploma 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 Diploma 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 Diploma 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
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.
Teachers
Intended learning outcomes
- Critically assess the relevance of theories of data modelling for efficient pipeline creation.
- Critically evaluate diverse scholarly views on best practices in developing data-intensive applications.
- Acquire knowledge of various methods for warehousing data.
- Develop a specialised knowledge of standard tools for data processing, such as Apache Kafka, Airflow, and Spark (with PySpark), and the Hadoop Ecosystem.
- Develop a critical understanding of data engineering.
- Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
- Creatively apply various visual and written methods for dashboarding data with Grafana/Tableau
- Autonomously gather material and organise it into a coherent presentation or essay
- Apply an in-depth domain-specific knowledge and understanding of orchestrating complete ETL pipelines.
- Solve problems and be prepared to take leadership decisions related to developing pipelines to handle massive datasets for engineering purposes.
- Demonstrate self-direction in research and originality in creating advanced SQL queries.
- Act autonomously in identifying research problems and solutions related to developing for data at scale.
- Efficiently manage interdisciplinary issues that arise in connection to developing cloud solutions for data engineering problems.
- Create synthetic contextualised discussions of key issues related to the data engineering lifecycle.
- Apply a professional and scholarly approach to research problems pertaining to data warehousing and modelling.
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 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. 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.
Teachers
Intended learning outcomes
- Critically assess the relevance of theories of data visualisation for business applications in the realm of software engineering.
- Critically evaluate diverse scholarly views on advanced visualisation strategies.
- Develop a specialised knowledge of such concepts as bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping.
- Develop a critical understanding of key data science concepts as implemented in common software packages.
- Acquire knowledge of various methods for telling stories with data across different formats.
- Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
- Apply an in-depth domain-specific knowledge and understanding of the importance of data storytelling in software engineering.
- Autonomously gather material and organise it into a coherent presentation or essay.
- Creatively apply various visual and written methods for developing data visualisations.
- Create synthetic contextualised discussions of key issues related to time and space complexity in data science.
- Solve problems and be prepared to take leadership decisions related to data visualisation strategies.
- Efficiently manage interdisciplinary issues that arise in connection to advanced visual analytics.
- Apply a professional and scholarly approach to research problems pertaining to data visualisations, including dashboards and storytelling.
- Demonstrate self-direction in research and originality in solutions developed for data visualisation.
- Act autonomously in identifying research problems and solutions related to implementing data science visualisations from scratch.
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 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.
Teachers
Intended learning outcomes
- Analyse different types of data and their impact on model selection.
- Explain how data science techniques are applied to extract insights that inform strategic business decisions across various industries.
- List and describe essential data science principles, including data wrangling, statistical analysis, and predictive modelling.
- Assess the accuracy, precision, recall, and other performance metrics of various models, comparing their effectiveness for different types of data.
- Apply data cleaning and preprocessing techniques to real-world datasets.
- Create and evaluate statistical models, such as linear regression and logistic regression, to analyse datasets and derive meaningful insights.
- Critically assess and evaluate the ethical implications of data science techniques.
- Create comprehensive workflows that include data collection, preprocessing, modelling, and evaluation, tailored to solve particular real-world challenges.
- Work effectively with team members from diverse backgrounds to design, implement, and present data science solutions, demonstrating strong teamwork and communication skills.
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.
About
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.
Teachers
Intended learning outcomes
- Analyse case studies of successful AI implementations in industry, discussing the challenges, solutions, and outcomes.
- Identify and describe the key applications of AI in various industries, such as healthcare, finance, manufacturing, and transportation.
- Explain how AI technologies are used to optimise processes like supply chain management, predictive maintenance, and customer service within different industries.
- Implement AI solutions in a simulated industry environment, demonstrating the application of AI tools and technologies to real-world scenarios.
- Evaluate the impact of AI solutions on business operations, including improvements in efficiency, cost reduction, and customer satisfaction.
- Develop AI models tailored to address specific challenges in industries such as healthcare, finance, or logistics using appropriate tools and techniques.
- Lead cross-functional teams in the development and deployment of AI projects within an industry, ensuring collaboration and successful implementation.
- Demonstrate the ability to adapt existing AI solutions to meet emerging needs and challenges within an industry, ensuring the AI applications remain relevant and effective.
- Demonstrate the ability to design AI-driven strategies that can transform industry practices, addressing current limitations and leveraging AI for competitive advantage.
About
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.
Teachers
Intended learning outcomes
- Analyse the impact of feature selection and engineering on model performance.
- Explain the concepts of overfitting, underfitting, model accuracy, precision, recall, and other evaluation metrics used in machine learning.
- List and describe various machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques, and their typical use cases.
- Evaluate the performance of machine learning models on different datasets.
- Develop and fine-tune machine learning models for specific applications.
- Implement machine learning algorithms using Python and relevant libraries.
- Critically assess the ethical concerns related to machine learning, such as bias, privacy, and transparency, and propose solutions to mitigate these issues.
- Collaborate on machine learning projects in a team environment to develop, test, and deploy machine learning models, demonstrating strong communication and project management skills.
- Design and deploy machine learning solutions to solve industry-specific problems.
About
No description available.
Teachers
Intended learning outcomes
Postgraduate Diploma in Artificial Intelligence
Downloads
Apply Now
Ready to start your journey? Apply for Postgraduate Diploma in Artificial Intelligence today.