Integrated Artificial Intelligence

In this course, you will explore the fundamental concepts in web development and cloud technology, and get hands-on practice creating interactive website and web applications by mastering both front-end and back-end technologies. You'll also explore cloud technologies, including the use and management of cloud compute services, service providers, security and migration strategies.

This course is designed to explore the intersection of Artificial Intelligence (AI) and Human-Centered design principles. It refers to the design, development, and deployment of Artificial Intelligence (AI) systems that prioritize and consider the needs, values, and well-being of human users. It involves designing AI technologies and applications with a focus on enhancing human experiences, augmenting human capabilities, and addressing social challenges. It emphasizes that AI systems should be created to serve and benefit humans, respecting human rights, privacy, and dignity. Students will learn how to design AI systems that prioritize human experiences and align with ethical guidelines. Students will gain deep understanding of the ethical considerations, user-centered design methodologies, and social impact of AI systems through a combination of theoretical knowledge, and practical exercises.

The Natural Language Processing (NLP) course is designed to provide students with a comprehensive understanding of the techniques and algorithms used in NLP, a subfield of AI that focuses on the interaction between computers and humans using natural language. The course covers the fundamental concepts of NLP, including text processing, sentiment analysis, text classification, and named entity recognition. Students will learn about the latest NLP techniques and algorithms, including deep learning approaches, and will gain hands-on experience using these methods to build NLP models. Throughout the course, students will have opportunities to work on real-world NLP projects and applications, applying the knowledge and skills they have gained to real-world challenges. Students are encouraged to use the knowledge from this course in their Capstone Applied Project.

Pre-requisites:

• ARTI 406
• STAT 400

This course provides a comprehensive understanding of the fundamental concepts of computer vision, including image processing, object detection, image segmentation and facial recognition. You'll explore the latest computer vision techniques and algorithms, and get hands-on experience using these methods to build computer vision models. You'll also examine computer vision applications, such as autonomous vehicles, medical imaging and security systems. The course concludes with an exploration of emerging trends in computer vision systems and real-world applications including robotics, surveillance and manufacturing.

Pre-requisites:

• ARTI 404
• ARTI 406

The AI Management and Maintenance course is designed to provide students with a comprehensive understanding of the processes and best practices required to manage and maintain AI systems and applications. The course covers the fundamental concepts of AI operations and maintenance, including system administration, performance tuning, and disaster recovery. Students will learn about the latest tools and technologies used in AI operations and maintenance, including cloud-based services, and will gain hands-on experience using these tools to manage and maintain AI systems. In addition, the course covers important aspects of AI governance, including data privacy and security, compliance, and ethics. Students will learn how to ensure that AI systems are operating in accordance with regulatory requirements and ethical principles, and will gain a deep understanding of the legal and ethical implications of AI. Throughout the course, students will have opportunities to work on real-world AI operations and maintenance projects, applying the knowledge and skills they have gained to real-world challenges. Students will receive guidance and mentorship from experienced AI professionals, including instructors and industry experts through guest lectures, as they work on their projects. The AI Management and Maintenance course is an important component of an AI post-diploma program, providing students with the knowledge and practical experience necessary to succeed in a career in the field of AI operations and maintenance. The course emphasizes hands-on learning and provides students with opportunities to apply their knowledge and skills to real-world AI operations and maintenance projects, which helps to ensure that they are well-prepared for the demands of the industry.

Pre-requisites:

• ETHI 401

This course focuses on the concepts, techniques and algorithms used in predictive modelling and data analysis. Students will propose solutions for business problems, refine and manipulate data, and use statistical analysis and machine learning models to develop analytical solutions. Students will also gain hands-on experience building machine learning models to predict the outcomes of given events and designing experiments to evaluate model performance.

Predictive Analytics & Modeling course is designed to build upon the foundation of the Predictive Analytics and Modeling Fundamentals. This course cover from intermediate to more advanced topics in predictive modeling, including unsupervised learning, ensemble learning, deep learning, reinforcement learning, and neural networks. Students will learn about the latest techniques and algorithms used in these areas and will gain hands-on experience using these methods to build predictive models. Students will learn how to analyze complex data sets and make predictions about future events. The course also covers important aspects of deployment, such as the ethics of AI, privacy and security, and explainability. Students will learn how to build responsible AI systems that take into account the ethical implications of AI and data privacy.

Pre-requisites:

• ARTI 406
• DATA 440
• STAT 400

This course provides students with a comprehensive understanding of AI ethics and how they can be leveraged in AI system development and usage. Topics include AI development, principles of responsible AI, and policies and regulations concerning AI ethics in major jurisdictions. Using real-world case studies, students will also examine ethical concerns that may arise from using AI in organizations. Having gained a strong foundation, students will be equipped to develop responsible and ethical AI systems, as well as advocate for AI ethics as users.

In this capstone course, you have the opportunity to apply the knowledge and skills you've gained throughout the program. This course emphasizes hands-on learning and gives you the chance to work on projects that address real-world AI challenges and problems.

Pre-requisites:

• ARTI 404
• DATA 440
• ETHI 401

This course teaches R and introduces Python and is designed to provide students with a comprehensive understanding of statistical techniques and their applications in the field of AI. The course covers the fundamentals of statistics, including descriptive statistics, probability theory, hypothesis testing, and regression analysis. Students will learn how to use statistical techniques to analyze and interpret data and will gain hands-on experience using the R programming language to perform statistical analyses.

This cooperative work term course provides the opportunity to apply your classroom learning in a practical workplace setting, as well as gain valuable industry experience. This course is optional.

Pre-requisites:

Manual Prerequisite

One of the following sets of pre-requisites, as applicable to your program of study:

Information Technology Services

• CPNT 224, CPRG 217, CPSY 204, CPSY 206, and PHIL 241

Software Development

• CPRG 211, CPRG 250, CPSY 200, CPSY 202, and PHIL 241

Information Systems Security

• ITSC 204, ITSC 205, ITSC 206, ITSC 302, and LAWG 200

New Media Production & Design

• MMDA 225, MMDA 237, MMDA 243, MMDA 244, and MMDA 247

Film and Video Production

• FVDO 250, FVDO 252, FVDO 254, FVDO 256, and FVDO 258

Journalism

• JOUR 251, JOUR 254, JOUR 258, PHOT 256, and PUBL 261

Interactive Design

• CPRG 218, DSGN 221, DSGN 227, MMDA 203, and PMGT 200

Integrated Artificial Intelligence

• ARTI 407, ARTI 408, ARTI 409, DATA 480, and PROJ 407