Master of Artificial Intelligence Design & Development (MAI)

Students will learn the basics and more advanced concepts of machine learning. These concepts will cover ML model building and applying algorithms to estimate these ML models. They will learn data preparation and how data is cleaned and explored to identify key patterns and trends to further refine the data. They will learn both unsupervised and supervised machine learning. Students will learn how to evaluate different ML models based on their ability to accurately predict. They will learn how to fine tune the hyperparameters of ML algorithms. They will learn ML model reporting and deployment.

Students will understand the mathematics of ML algorithms. Specifically, they will learn how to mathematically derive commonly used formulas such as Ordinary Least Squares (OLS). Student will also learn the mathematics behind linear and non-linear ML algorithms. They will also learn how to gauge the validity and performance of machine learning algorithms. They will become will learn to use mathematics to gain a deeper understand of algorithms that are commonly used in the industry today.

Students will learn the both the fundamental and practical aspects of Data Science. They will understand the data science process as it relates to data, ML model formulation, building, and using algorithms to estimate these ML models. Students will become proficient in the entire data science process. They will also learn how to break down business problems and formulate them in ML models that are estimated with algorithms. They will learn how to interpret ML algorithm results and verify and validate ML model results. Lastly, students will learn how to present the ML model results in visualization dashboards for data driven decision making.

Students will learn how AI projects are managed. Specifically, students will understand the unique features of AI projects relative to conventional (non-AI) projects. Student will understand how to define AI projects such as common AI tasks, resources, risks, challenges, technologies, and skills. Students will understand how AI projects are executed and controlled. They will also learn how to manage stakeholders and set expectations regarding AI results. Students will also learn how to establish a business case for AI projects. They will also learn how AI projects are costed out. Lastly, students will learn the process around AI solution deployments in operations.

This course provides students with knowledge of ethical challenges, concerns and issues with AI. Specifically, how to ethical issues impact AI development and how are these issues addressed. Students will learn a variety of approaches and strategies to deal with ethical issues with AI and learn how to apply them in AI solutions.

This course introduces students to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on the cloud. Students will learn how to build, test, and deploy ML models.

This course will cover the foundations of Deep Learning, including the building and training of multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course will give students the ability to create deep learning architectures that can handle many different types of data such as images, texts, voice/sound, graphs, among others.

In this course, students will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information and the operations of how generative AI is used and operationalized. From word representation and syntactic processing to designing and implementing complex deep learning models for question answering, machine translation, and other language understanding tasks, students will tackle a variety of tasks using elementary to advanced NLP approaches.

This course provides students with the theory and practical knowledge to approach computer vision related tasks, such as image formation, classification, feature detection and matching, segmentation, motion estimation and tracking, and classification. Students will learn about a variety of Computer Vision algorithms and architectures, such as Convolutional Neural Networks (CNNs) and their applications.

In this course, students will gain the skills necessary both to stay abreast of ongoing developments in the field, and to develop their own research. Students will learn to read and evaluate research publications, as a primary tool to build and maintain their knowledge of the continually evolving state-of-the-art, and as a foundation for their own research. In seminar format, students will practice these skills, while at the same time expanding their technical knowledge, by analyzing, presenting, and discussing a selection of important publications. Students will also learn how to plan applied research projects, building a plan for their own project.

Students will develop the applied research project incorporating their ARP project plan from MAI205 – AI Research and develop and integrate their solution. Students will work on the project independently with guidance from an academic supervisor. Students will present their findings with a presentation and will submit either a report or portfolio with their findings.

The internship provides students with professional experience across various industries and sectors. Students will have the opportunity to apply the knowledge and skills acquired during their academic studies while gaining new competencies in a real-world work environment. They will engage with industry professionals, enhancing their critical thinking, problem-solving, and decision-making abilities in a practical setting. Throughout the internship, students will gain further insight into workplace dynamics, including understanding their role within the team and the broader organization. The experience will also allow students to refine their communication skills and adapt them to meet industry-specific demands and job functions. This internship is designed to support career development, facilitate the transition from academic to professional life, and enhance graduate outcomes through hands-on learning and industry mentorship, making it particularly valuable for those seeking to advance or transition their careers.

Students will develop the applied research project incorporating their ARP project plan, ARP Dataplan, ARP Methodology and ARP Literature Review assignments from their previous courses and develop and integrate their solution. Students will work on the project independently with guidance from an academic supervisor. Students will present their findings with a presentation and will submit either a report or portfolio with their findings.

The internship provides students with professional experience across various industries and sectors. Students will have the opportunity to apply the knowledge and skills acquired during their academic studies while gaining new competencies in a real-world work environment. They will engage with industry professionals, enhancing their critical thinking, problem-solving, and decision-making abilities in a practical setting. Throughout the internship, students will gain further insight into workplace dynamics, including understanding their role within the team and the broader organization. The experience will also allow students to refine their communication skills and adapt them to meet industry-specific demands and job functions. This internship is designed to support career development, facilitate the transition from academic to professional life, and enhance graduate outcomes through hands-on learning and industry mentorship, making it particularly valuable for those seeking to advance or transition their careers.