Bachelor’s Degree in Computer Science with Specialization in AI & ML

Optimizing Your Learning: Academic & Critical Thinking Skills

Through the coursework, students will learn to identify and evaluate arguments, analyze data and evidence, and draw logical conclusions. Also, students will learn to write clear and concise academic papers, citing sources and avoiding plagiarism. They will also learn to use proper grammar and punctuation and to organize their writing in a logical and coherent manner. In addition, the module will cover research skills, including how to find and evaluate  sources and how to use them effectively in academic writing. Students will also learn how to use technology tools to support their research and writing, such as reference managers and citation software. By the end of the module, students will have gained the critical thinking and writing skills necessary to succeed in their academic and professional careers in AI and They will be able to analyze complex problems, communicate their ideas effectively, and produce high-quality research papers and reports.

Learning Outcomes

1. Define critical thinking and its importance in academic writing
2. Discuss the characteristics of effective academic writing

Communicating for Success

The module supports students in developing communication skills that are essential for success in their personal and professional lives. The course will focus on close reading, written communication, verbal communication, and non-verbal communication skills. An emphasis will be placed on weekly submissions and peer and instructor feedback to allow students to practice and improve their skills.  Students will learn how to effectively read and analyze texts as a precursor to developing their own written communication skills. They will then practice crafting clear communications by learning about topics such as writing structure and organization, grammar, audience awareness, and the iterative writing process. Next, students move on to verbal communication and will learn how to confidently and skillfully deliver effective oral presentations. Finally, students will learn about the impact of non-verbal communication on how their messages are received.  The course will culminate in a project that will require students to develop and implement a strategy for communicating a technical topic to a non-technical audience. 

Learning Outcomes

1. Understand writing structure and organization, grammar, the role of audience awareness, and the iterative writing process, demonstrated by delivering effective written and oral presentations.

2. Cultivate close reading skills, written communication skills, verbal communication skills, and non-verbal communication skills

Mathematical Thinking

This module aims to provide students with a foundational understanding of linear algebra and calculus and their applications in mathematics and other fields. Students will learn the basic concepts of linear algebra, including vectors, matrices, and linear transformations, and understand how they are used in various mathematical fields. By the end of the module, students will have gained a solid understanding of the mathematical concepts and tools used in AI and will be able to apply them to real-world problems. They will be able to analyze and interpret data, develop and evaluate mathematical models, and optimize algorithms for efficient and effective AI applications. This course helps students develop the ability to think logically and mathematically. It prepares students for more advanced courses in algorithms and discrete mathematics. An emphasis is placed on the ability to reason logically and effectively communicate mathematical arguments. 

Learning Outcomes

1. Develop the ability to think logically and mathematically at a level that prepares students for more advanced courses in algorithms and discrete mathematics.
2. Display creativity and initiative in carrying out algebraic operations necessary to perform programming functions.
3. Display knowledge of algebraic operations in order to perform programming functions.

Operating Systems

The module aims to classify different types of operating systems, including Windows, macOS, and Linux, and to describe the functions of an operating system, such as process and memory management. Through the course, students will learn about the architecture and components of operating systems, including user interfaces, device drivers, and file systems. They will also gain an understanding of system calls and APIs, and how to use them to interact with an operating system.  By the end of the module, students will have gained a comprehensive understanding of operating systems, their functions, and their importance in computer science and AI. They will be able to identify the different types of operating systems and describe their functions and features. This knowledge will prepare them for more advanced courses in the curriculum that involve developing AI and ML applications on different operating systems.

Learning Outcomes

1. Classify the different types of operating systems, such as Windows, macOS, and Linux.
2. Describe the functions of an operating system, such as process management and memory management.

Computer Systems: Computer System Architecture

This module provides a comprehensive understanding of computer system architecture, focusing on the instruction set architecture (ISA), memory systems, and instruction formats. Students will learn about the basic concepts of ISA, including instruction types and operand types, and explore the different types of ISA, such as CISC and RISC. They will also gain practical knowledge in designing computer systems based on ISA principles. In addition, students will learn about different memory systems, including cache memory and virtual memory, and how they are used in modern computer systems. Finally, they will learn about different instruction formats used in computer system architecture and how they impact system performance.  By the end of this module, students will have a thorough understanding of computer system architecture, including ISA, memory systems, and instruction formats. They will be able to design and optimize computer systems for specific applications and evaluate the performance impact of various design decisions. This module provides a strong foundation for students pursuing further studies in computer science, computer engineering, or related fields.

Learning Outcomes

1. Explain the concept of instruction set architecture and its impact on computer system design.
2. Identify the different types of memory systems used in modern computer architecture.
3. Classify the different types of instruction formats used in computer system architecture.

Database Management

The module’s primary learning outcomes are for students to identify different types of database management systems, describe their components, and explain the importance of database normalization. Through the course, students will learn about database design, normalization, and optimization. They will also learn how to use SQL to manipulate and retrieve data from databases. The module emphasizes hands-on learning through database design and development projects. By the end of the module, students will have gained a comprehensive understanding of database management systems and their importance in AI and ML applications. They will be able to identify different types of database management systems and their components and apply the concepts of database normalization to design and develop efficient databases. This knowledge will prepare them for more advanced courses in the curriculum and for database management roles in the industry.

Learning Outcomes

1. Identify the different types of database management systems, such as relational and NoSQL.
2. Describe the components of a database management system, such as tables and indexes

Programming 1

The course helps students develop an appreciation for programming as a problem-solving tool. It teaches students how to think algorithmically and solve problems efficiently and serves as the foundation for further computer science studies. Using a project-based approach, students will learn to manipulate variables, expressions, and statements in Python and understand functions, loops, and iterations. Students will then dive deep into data structures such as strings, files, lists, dictionaries, tuples, etc. to write complex programs. Over the term, students will learn and apply basic data structures and algorithmic thinking.  Finally, the course will explore the design and implementation of web apps in Python using the Flask framework. Throughout the course, students will be exposed to abstraction and will learn a systematic way of constructing solutions to problems. They will work on team projects to practice pair programming, code reviews, and other collaboration methods common to the industry. The course culminates in a final group project and presentation during which students demonstrate and reflect on their learning.

Learning Outcomes

1. Independently manage projects that require programming as a problem-solving tool, requiring the manipulation of variables, expressions, and statements. 
2. Display creativity and initiative in writing complex programs requiring the application of knowledge of basic data structures and algorithmic thinking into code using the fundamentals of programming.
3. Propose appropriate solutions to well-scoped but abstract and changing problems pertaining to the implementation of programming methods that require a knowledge of functions, loops, and iterations.

Web Application Development: Web Programming

This module focuses on building web applications and covers the roles of HTML, CSS, and JavaScript in their development. Students will learn how these technologies work together to create modern web applications. They will explore the key features of HTML, such as semantic markup, forms, and multimedia, and learn how to use CSS to style and layout web pages.  Additionally, students will learn how JavaScript can be used to add interactivity and dynamic behavior to web applications. Through hands-on exercises and projects, students will gain practical knowledge and experience in building web applications. Students will learn how to translate a given design specification into a functional web application using appropriate HTML, CSS, and JavaScript coding practices. By the end of this module, students will have a thorough understanding of building web applications using HTML, CSS, and JavaScript. They will be able to identify different types of web application architectures and use appropriate coding practices to translate design specifications into functional web applications.

Learning Outcomes

1. Explain the roles of HTML, CSS, and JavaScript in building web applications and how they work together.
2. Identify the different types of web application architectures, such as client-server and peer-to-peer, and compare their advantages and disadvantages.
3. Translate a given design specification into a functional web application using appropriate HTML, CSS, and JavaScript coding practices.

Fundamentals of AI And ML

This module introduces the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). Students will learn the definition of AI and ML, their evolution, and their applications in various fields. They will also explore the different types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Through hands-on exercises and case studies, students will gain practical knowledge and experience in applying machine learning algorithms to real-world problems. Moreover, this module covers the principles of selecting the appropriate machine learning algorithm for a given problem. Students will learn about the factors that influence algorithm selection, such as data type, problem complexity, and performance requirements. They will also explore the principles of model training, validation, and testing, and gain practical knowledge and experience in evaluating machine learning models. By the end of this module, students will have a thorough understanding of AI and ML, be able to identify different types of machine learning algorithms and select the appropriate algorithm for a given problem.

Learning Outcomes

1. Define artificial intelligence and machine learning.
2. List the different types of machine learning algorithms.
3. Select the appropriate machine learning algorithm for a given problem.

Emerging Technologies in AI

Through the course, students will recognize emerging technologies in AI, describe their potential impact on society and industry, and discuss their ethical and social implications. By the end of the module, students will have gained a comprehensive understanding of emerging technologies in AI and their impact, preparing them to make informed decisions about the adoption and development of AI technologies in their future roles.

Learning Outcomes

1. Recognize the emerging technologies in AI, such as deep learning and natural language processing.
2. Describe the potential impact of emerging technologies on society and industry

Industry Experience 1

Through the course, students will define and identify industries using AI, recognize applications of AI in different industries, and explain technical aspects of AI to non-technical stakeholders. By the end of the module, students will have gained practical experience in the application of AI technologies in industry settings, preparing them for careers in AI and ML roles across various industries.

Learning Outcomes

1. Define and identify the various industries that are using AI, such as healthcare, finance, and manufacturing.
2. List and recognize the applications of AI in different industries, such as predictive maintenance, fraud detection, and personalized medicine.
3. Translate and explain the technical aspects of AI to nontechnical stakeholders in an industry setting.

Discrete Math

This course builds on Mathematical Thinking and provides the mathematical foundation needed for many fields of computer science, including data science, machine learning, and software engineering. It focuses on core mathematical areas that are essential in the toolkit of every computer scientist: logic, combinatorics and probability, set theory, graph theory, and elementary number theory. Each topic is covered with a focus on applications in modern computer science. It begins with a unit on logic which builds on previous knowledge, with an emphasis on writing readable and precise code. Probability and combinatorics focus on the analysis of algorithms and reliability. There is an in-depth focus on graph theory, and students explore the numerous applications of graph theory in computer science (data mining, clustering, networking, etc.). Finally, the course introduces number theory, beginning with fundamental results such as the Euclidean Algorithm and then applications in cryptography. The course culminates in a final group project where students explore original mathematical sources and describe the historical proof techniques of a discrete math topic.

Learning Outcomes

1. Write readable and precise code that demonstrates an in-depth knowledge of discrete math.

2. Mathematical areas that are essential in the toolkit of every computer scientist: logic, combinatorics and probability, set theory, graph theory, and elementary number theory. 

3. Mathematical areas that are essential in the toolkit of every computer scientist: logic, combinatorics and probability, set theory, graph theory, and elementary number theory.

Engineering for Development

Engineering for Development is a Course that helps students investigate the role that technology can play in solving some of the world’s most intractable social and economic development challenges. In Engineering for Development, students will learn how to analyze the root causes of development challenges so that they are able to build effective technology solutions. The course aims to introduce students to selected global development challenges using the United Nations Sustainable Development Goals (SDGs) as the framework for selecting the areas of focus. Each term, the course will focus on 1- 2 subject areas (e.g. Quality Education, Affordable and Clean Energy, Climate Action), which will serve as test cases for students to develop the skills required to effectively analyze and understand complex development issues. Students will examine the system-level dynamics that are at the root of these challenges and will also analyze and critique technology-related solutions that have been developed to address these challenges.

Learning Outcomes

1. Demonstrates administrative planning, resource management, and team management.
2. Key strategies for decomposing problems into actionable engineering solutions.
3. Analyze the root causes of development challenges, formulating and executing an effective technology solution.

Network and Computer Security: Computer Network

This module covers the principles of Computer Networks, which are essential for efficient data communication. Students will learn to design and implement computer networks using different network topologies and protocols. They will explore the principles of network architecture, transmission media, network devices, and network services, and gain practical knowledge and experience in configuring and troubleshooting computer networks. Additionally, students will learn to use network monitoring tools to schedule and optimize network performance and troubleshoot network problems. Furthermore, this module covers the principles of different network models and their suitability for specific applications. Students will learn to organize and differentiate between different network models, such as peer-to-peer, client-server, and hybrid networks. They will gain practical knowledge and experience in selecting the most suitable network model for a specific application, taking into account the scalability, security, and efficiency of the network.

Learning Outcomes

1. Demonstrate an understanding of different network topologies and protocols to design and implement a computer network.
2. Use and operate network monitoring tools to schedule and optimize network performance and troubleshoot network problems.
3. Organize and differentiate between different network models to choose the most suitable one for a specific application.

Data Structures and Algorithms 1

This course provides an introduction to data structures and algorithms, covering basic concepts and implementations. Students will learn how to implement and analyze data structures like arrays, linked lists, and trees. They will also develop and optimize algorithms for searching and sorting data, and evaluate the performance of different data structures and algorithms. The course prepares students for more advanced topics in data science and machine learning.

Learning Outcomes

1. Implement and analyze basic data structures, such as arrays, linked lists, and trees.
2. Develop and optimize algorithms for searching and sorting data.
3. Evaluate the performance of different data structures and algorithms in terms of time and space complexity.

Python for Machine Learning

This module is designed to provide students with the necessary knowledge and skills to use Python programming language for developing and implementing machine learning algorithms. Students will learn how to use popular Python libraries such as NumPy, Pandas, and Scikit-learn to perform data preprocessing, feature engineering, and model training. They will also be able to analyze and interpret machine learning results using Python and make data-driven decisions based on them. Through practical exercises and projects, students will gain hands-on experience in using Python for machine learning and be well-prepared for a career in the field of artificial intelligence and machine learning.

Learning Outcomes

1. Use Python libraries such as NumPy, Pandas, and Scikit-learn to implement machine learning algorithms.
2. Demonstrate proficiency in using Python for data preprocessing, feature engineering, and model training.
3. Analyze and interpret machine learning results in Python and make data-driven decisions based on them.

Explorative Data Analysis and Visualization

This module covers the fundamentals of explorative data analysis and visualization. Students will learn how to execute exploratory data analysis techniques and interpret the results to identify patterns and trends in data. They will also learn how to use data visualization tools to present and communicate insights from data. The module will teach students to compare and contrast different visualization techniques and select the appropriate one for a given data set.

Learning Outcomes

1. Execute and interpret exploratory data analysis techniques to identify patterns and trends in data.
2. Use data visualization tools to present and communicate insights from data.
3. Compare and contrast different visualization techniques and select the appropriate one for a given data set.

Challenge Studio 1

The module helps students investigate the role that technology can play in solving some of the world’s most intractable social and economic development challenges. In Challenge Studio 1, students will work in groups to design, develop, and test a solution to a development challenge of their choice. The focus of this course is to provide students with the tools and skills to create meaningful technology solutions (e.g. services, products) to a sustainable development problem. 

Learning Outcomes

1. Work as a team to develop, using core strategies of problem formulation; user research; and build, measure, learn cycles, a Minimum Viable Product or prototype that provides a practical solution for an identified problem. 
2. Human-centered design principles, end-user identification strategies; best practices for requirements gathering and impact measurement.

Cyber Security Fundamentals

This module covers the basics of cybersecurity threats, principles, and measures. It equips students with the knowledge to identify different types of cyber attacks, understand cybersecurity principles, and select appropriate security measures to safeguard against attacks. The course is designed to ensure that students have a foundational understanding of cybersecurity, which is increasingly important in today’s digital landscape where cyber threats are becoming more sophisticated and prevalent. Furthermore, this module covers the principles of security monitoring and incident response. Students will learn to analyze and interpret security event data to detect and respond to security incidents. They will gain practical knowledge and experience in using security incident response procedures, including identification, containment, eradication, and recovery. Additionally, students will learn to document security incidents and produce incident response reports.

Learning Outcomes

1. Define and identify the different types of cyber threats and attacks, such as malware, phishing, and denial-of-service attacks.
2. Discuss and explain the principles of cybersecurity, including risk management, security policies, and security best practices.
3. Execute and operate basic security tools and techniques to identify and remediate security vulnerabilities in computer systems and networks.

Industry Experience 2

In this module, students will have the opportunity to gain practical industry experience by working on real-world projects. They will be able to apply the concepts and techniques they have learned in the classroom to solve complex problems and analyze data in various industries. Through this experience, they will be able to enhance their critical thinking skills, evaluate proposed solutions, and effectively communicate their findings to stakeholders. This module aims to bridge the gap between theoretical knowledge and practical application, providing students with a valuable opportunity to gain hands-on experience and prepare for their future careers in AI and ML.

Learning Outcomes

1. Demonstrate proficiency in applying concepts and techniques learned in the classroom to solve real-world problems in industry.
2. Analyze and interpret data and results from industry projects and communicate them effectively to stakeholders.
3. Apply critical thinking skills to evaluate the feasibility and effectiveness of proposed solutions to industry problems.

Ethics and Social Implications of AI

In this course, students will discuss ethical considerations in AI, including bias and privacy concerns, and describe social implications, such as the impact on employment and the economy. Students will also identify stakeholders affected by AI and their respective interests. By the end of the module, students will have a comprehensive understanding of ethical and social considerations in AI, preparing them to make informed decisions about the development and use of AI technologies in their future roles.

Learning Outcomes

1. Discuss the ethical considerations in AI, such as bias and privacy concerns.
2. Describe the social implications of AI, such as the impact on employment and the economy.

Digital Marketing and Analytics

This module covers the principles of Digital Marketing and Analytics, which involves the use of digital channels to promote products or services and measure their performance. Students will learn to use Google Analytics to optimize digital marketing campaigns and develop a social media marketing plan that reaches target audiences. They will gain practical knowledge and experience in understanding the principles of search engine optimization (SEO), pay-per-click advertising (PPC), email marketing, and content marketing. Additionally, students will learn to evaluate the effectiveness of digital marketing strategies using relevant analytics tools to enhance online brand presence and customer engagement. Furthermore, this module covers the principles of social media marketing and analytics and the knowledge to use social media analytics tools to evaluate the effectiveness of social media marketing strategies.

Learning Outcomes

1. Use Google Analytics to optimize digital marketing campaigns.
2. Develop a social media marketing plan that reaches target audiences.
3. Evaluate the effectiveness of digital marketing strategies using relevant analytics tools to enhance online brand presence and customer engagement.

Natural Language Processing Fundamentals

This module is designed to equip students with the fundamental concepts and techniques used in natural language processing (NLP). Students will learn how to perform basic NLP tasks such as tokenization, stemming, and part-of-speech tagging. They will also learn about machine learning models for NLP, including supervised and unsupervised learning, and how to evaluate the performance of these models. Additionally, students will gain practical experience designing and developing custom NLP models for specific applications, such as sentiment analysis and named entity recognition. By the end of this module, students will have a solid understanding of NLP and be able to apply their knowledge to real-world problems.

Learning Outcomes

1. Operate and apply basic natural language processing techniques, such as tokenization and part-of-speech tagging.
2. Schedule and analyze the performance of machine learning models for natural language processing tasks, such as sentiment analysis and named entity recognition.
3. Sketch and design custom natural language processing models for specific applications.

Computer Vision Fundamentals

In this module, students will learn about the fundamental concepts and techniques used in computer vision. They will explore image processing techniques such as edge detection and image segmentation, and how these techniques are used to analyze and interpret images. The course will cover various object detection algorithms such as YOLO and Faster R-CNN, and how they can be used to detect and classify objects in images. Additionally, students will be able to evaluate the performance of different computer vision models and apply them to real-world problems.

Learning Outcomes

1. Question and interpret the results of image processing techniques, such as edge detection and image segmentation.
2. Test and evaluate different feature extraction techniques, such as SIFT and SURF.
3. Compare and contrast different object detection algorithms, such as YOLO and Faster R-CNN.

Interaction Design

This course introduces students to the principles of human-computer interaction (HCI). Students explore how humans process information (perception, memory, attention) in the context of designing and evaluating interfaces. This course complements programming coursework by helping students understand how to design more usable systems. The course builds on previous knowledge of design thinking and expects students to apply the design thinking methodology as a starting point. The first part of the course focuses on designing for multiple platforms. Students create designs that solve a problem on multiple devices (e.g., web, mobile, wearables) and learn how to create a coherent design system as users move between devices. The second part of the course delves into design beyond visual user interfaces and teaches students how to design for emerging technologies, for example, sensors, controls and ubiquitous computing. Throughout the course, students learn and apply a variety of evaluation methodologies used to measure the usability of design. This is a project-based course and, in each part, students will work in a team to design, prototype and test a solution to a problem. Students will present their designs in class sessions and practice giving and receiving meaningful critiques.

Learning Outcomes

1. Design, prototype, and test a solution to an HCI problem using techniques taught in the course.
2. Sufficient knowledge of Human-Computer Interaction to work in a team to design, prototype and test a solution to a problem.
3. Evaluate interfaces, identify problems, and design solutions using insights from the principles of Human-Computer Interaction.

Backend development

Backend Development builds on previous knowledge of web development and security, and equips students with knowledge of server-side development so that they can become professional back-end developers and build enterprise-scale applications. Students learn to develop and deploy server-side applications with greater scope and complexity. In this project-based course, students deepen their understanding by building the back end for a cross-platform application. The project will require implementing advanced features that add complexity and uniqueness to a server’s structure. Examples of these include payment gateways, chat rooms, full-text search, WebSockets, etc. Students will design and build out all of the API endpoints needed for the application and properly secure them for use in any web or mobile front-end application. In doing so, they will explore the differences and tradeoffs between web services, APIs, and microservices. They will learn best practices for code quality, including unit testing and error handling. They will also learn to efficiently document their APIs. Students will understand key Developer Operations (DevOps) practices including environment design, testing, development controls, and uptime management. They will implement modern DevOps workflows (e.g., containers, cloud virtual machines), and learn tradeoffs between different approaches. They will set up continuous integration and continuous delivery, and explore various strategies for automated testing and application monitoring.

Learning Outcomes

1. Understand and make reasonable decisions about key Developer Operations (DevOps) practices, including environment design, testing, development controls, and uptime management
2. Design and build out all of the API endpoints needed for a web application and properly secure them for use in any web or mobile front-end application
3. Implement modern DevOps workflows (e.g., containers, cloud virtual machines), and learn tradeoffs between different approaches

Applied AI & ML Project Management

This module covers the principles of Applied AI & ML Project Management, which involves managing projects that utilize artificial intelligence (AI) and machine learning (ML) techniques to solve real-world problems. Students will learn to develop a project timeline that includes task dependencies and identifies potential project risks. They will gain practical knowledge and experience in understanding the principles of project management including project planning, resource allocation, risk management, and project monitoring. Additionally, students will learn to apply machine learning algorithms to solve problems in various domains such as healthcare, finance, and marketing. Furthermore, this module covers the principles of managing project resources effectively. Students will learn to identify and address resource constraints and ensure team members meet project milestones. They will gain practical knowledge and experience in understanding the principles of project scheduling, team communication, and performance monitoring. Additionally, students will learn to use project management tools and software to manage project resources effectively.

Learning Outcomes

1. Develop a project timeline that includes task dependencies and identifies potential project risks. 
2. Apply machine learning algorithms to solve problems in various domains such as healthcare, finance, and marketing. 
3. Manage project resources effectively by identifying and addressing resource constraints and ensuring team members meet project milestones.

Capstone Research Methods

The Capstone Research Methods course supports students in developing critical research skills that are needed for the successful completion of their capstone project (Applied Computer Science). The course provides students with an overview of the research process and types of capstone projects that they can undertake and includes a detailed exploration of relevant quantitative and qualitative research methods. Students will develop skills in data gathering and analysis, researching and writing an effective literature review, creating a research proposal, and managing ethical considerations with regard intellectual property rights and research with human subjects. At the conclusion of the course, students will be required to submit their formal capstone project proposal, which should include details of their project scope, research question, hypothesis, and project plan. Their proposal must receive a passing mark before they are allowed to undertake the capstone course in the final term of the degree program.

Learning Outcomes

1. Possess the academic competencies to plan a research project, evaluating the types of capstone projects that can be undertaken.
2.  Research planning strategies, demonstrated by the completion a formal project proposal which should include details of the project scope, research question, hypothesis, and project plan.
3. Understand and evaluate the range of potential tools and techniques used in research, including a detailed exploration of relevant quantitative and qualitative research methods to be used in the capstone.

Applied Computer Science

This capstone course enables students to demonstrate their proficiency in the technical and human skills that they have acquired throughout their undergraduate studies. The capstone requires students to conceptualize, plan, and implement a software project to completion, and evaluate their project’s processes and outcomes.  The capstone builds on the initial project scoping work that was carried out in Capstone Research Methods, which culminated in students submitting a project proposal, and gaining formal approval for their capstone Project Proposal. In this course, students will implement their proposed project with the support of a supervisor. Students with a common supervisor will be put into capstone advisory peer groups and will be required to meet with their group and supervisor regularly to update each other on their capstone progress and to provide feedback. Students will also have regular meetings with their capstone supervisor to provide additional support and guidance throughout the module. Upon completion of their capstone projects, all students will be required to participate in a capstone symposium at the end of the term, where they will present their working projects/prototypes to internal and external stakeholders.

Learning Outcomes

1. Evaluate and appraise the ethical and social implications of AI in industry, such as the impact on jobs and privacy.
2. Judge and support the effectiveness and efficiency of AI solutions implemented in industry.
3. Design and develop an AI solution for an industry problem, and present the solution and its potential benefits to stakeholders.