Applied AI Solutions Development (T431) FAQs 

Yes. Applicants from a wide range of academic and professional backgrounds are encouraged to apply. Previous students and applicants have come from areas such as psychology, product management, graphic design, mathematics, physics, medical bioscience, and other non-traditional fields. The admissions process assesses individual eligibility and credential equivalency, and applicants with unrelated credentials may require additional departmental review. 

Applicants are expected to have an intermediate level of Python proficiency or experience with another programming language. Students should already understand basic object-oriented programming concepts, external packages, data structures such as arrays, lists, and dictionaries, and control structures such as loops and conditional statements. Applicants who are completely new to programming and Python may find it difficult to keep pace with the program. 

Applicants are encouraged to strengthen their Python skills and complete introductory machine-learning courses before beginning. Resources such as Kaggle courses may be useful. Students are also expected to have a solid foundation in statistics and probability before entering the program. 

Applicants may be asked to complete a questionnaire after applying and describe their existing skills. This is not a test; rather, it asks applicants to provide their information.  

No specific degree, GPA, job title, or combination of work experience automatically guarantees admission. General guidance is that applicants from different academic backgrounds may apply, but programming ability, statistics, probability, and general quantitative preparation are important. Admissions reviews each applicant’s credentials and supporting documents individually. 

Applicants are encouraged to apply while space is still available and as soon as possible. Admission will stay open as long as space is available and time permits. Once an intake reaches capacity, additional applicants may be placed on a waiting list. Exact deadlines, current intake availability, document-submission deadlines, and expected decision dates should be confirmed with Admissions or through the college website. 

• Domestic applicants can contact Admissions at Admissions@georgebrown.ca 

• International applicants can contact intladmissions@georgebrown.ca.  

• Domestic registration questions can be directed to registration@georgebrown.ca. 

• International students with questions about registration, visas, or related matters can also contact the International Centre. 

Students generally take two courses at a time. One course may run on Monday, Wednesday, and Friday, while the second runs on Tuesday and Thursday. After completing those courses, students move on to the next two courses. Classes begin each semester with two courses that run for four weeks, then two courses that run for three weeks, then four weeks again, followed by three weeks. The next semester follows the same. 

The program is hybrid, with more than 50 per cent of classes delivered in person. The schedule will be provided for students. 

The third semester is a mandatory work-integrated learning (WIL) term. Students can complete this requirement in one of two ways: 

1. A student who secures a paid co-op position in a related field may complete the co-op placement.  

2. The other option is a Work Integrated Project, in which students complete an industry project.  

If students cannot secure a co-op or an industry Work Integrated Project, the polytechnic will assign a project. 

Students are responsible for finding and applying for their own paid co-op opportunities. The polytechnic assists by identifying or posting opportunities and helping students prepare for the job-search process, but employers decide which applicants they interview and hire. 

Students who do not secure a paid co-op are usually placed on an industry project with an industry partner.

The hiring company sets co-op requirements, and they may differ from one company to another. For detailed information and guidance on co-op, please contact the co-op office. 

The work-integrated-learning term lasts approximately four months. 

There is no requirement for this, but it is recommended that students at least begin searching and applying for placements before the end of the first semester. 

In most cases, yes, although individual employers or positions may have their own eligibility requirements. 

When several industry-project options are available, students may be able to select a project. In other situations, students may be matched with an available project. 

The program is designed to balance theory and practical work. Courses generally include approximately a 50/50 split between theoretical instruction and lab-based or practical work. Students learn machine-learning concepts, theory, and algorithms while applying them through programming, model development, labs, and projects. However, this depends on the course; some are more theoretical than others.

Students begin by learning machine-learning concepts, theory, and algorithms using existing tools. The program focuses on understanding how machine-learning and deep-learning systems function while also teaching students how to implement and apply them using modern frameworks and libraries. 

Yes. Students train and build models as part of the program and apply machine-learning and deep-learning techniques through coursework, labs, and projects.

Students are expected to enter the program with a good foundation in statistics. Mathematics in the program is focused primarily on the concepts and calculations needed to understand the functionality of machine-learning and deep-learning systems rather than on mathematics as a separate theoretical subject. 

This program is highly applied and hands-on; the goal is not to dive deep into the mathematical foundations of different concepts. However, students will review intuitions so they have a high-level understanding of how different methodologies work. In summary, this program does not focus on mathematics. 

Project work is a significant part of the program. Typically, most courses include either one large project or several smaller projects. This allows students to apply the theory, programming techniques, and machine-learning concepts covered in class. Some courses are exam-based. 

In many cases, students can select their own project topics and datasets.  

Yes. Students may work on generative-AI and agentic-AI projects, particularly when they choose these areas for project work.  

Deployment and some MLOps concepts are covered throughout the program. Students are introduced to considerations involved in moving machine-learning and AI solutions beyond development and into practical use. 

The program focuses on applied and practical AI skills, combining artificial intelligence, machine learning, data analytics, programming, and business applications. Students learn to apply data science and machine learning to real problems and work with technologies related to computer vision, large language models, AI agents, and frameworks such as Scikit-learn, Keras, and PyTorch.