Machine Learning Work Integrated Project II - CMPT 3835

This course is a continuation of CMPT 3830 (Machine Learning Work Integrated Project I). In this course, students work in groups on machine learning projects based on real-world business cases and solutions. Students will learn the project lifecycle and acquire the multidisciplinary teamwork and strategy skills required for employment. Students will work in domain-specific cohorts on applied machine learning business cases using problems and datasets developed by local companies while also exploring the business case for employing machine learning solutions. By working through applied machine learning projects, students will begin to develop a portfolio of work and learn the practical skills of conceptualizing and progressing through a basic project workflow as a team. This course is taught by local experts and industry guest lecturers, so students will have the opportunity to engage industry experts and network with potential employers. Examples of domain-specific cohorts may include health, fintech, social enterprise, and environmental science and natural resources. Students must complete this course in a term directly after CMPT 3830 . Note for international learners: This is not work-integrated learning, and there are no practicum hours assigned to this course. International learners do not require a co-op work study permit for this course.

Note: Prerequisite CMPT 3830 and CMPT 2150. Restricted to Machine Learning Analyst.

This course is a continuation of CMPT 3830 (Machine Learning Work Integrated Project I). In this course, students work in groups on machine learning projects based on real-world business cases and solutions. Students will learn the project lifecycle and acquire the multidisciplinary teamwork and strategy skills required for employment. Students will work in domain-specific cohorts on applied machine learning business cases using problems and datasets developed by local companies while also exploring the business case for employing machine learning solutions. By working through applied machine learning projects, students will begin to develop a portfolio of work and learn the practical skills of conceptualizing and progressing through a basic project workflow as a team. This course is taught by local experts and industry guest lecturers, so students will have the opportunity to engage industry experts and network with potential employers. Examples of domain-specific cohorts may include health, fintech, social enterprise, and environmental science and natural resources. Students must complete this course in a term directly after CMPT 3830 . Note for international learners: This is not work-integrated learning, and there are no practicum hours assigned to this course. International learners do not require a co-op work study permit for this course.

Note: Prerequisites: A minimum of a C- in each of CMPT 3830 and CMPT 2150. Corequisites: CMPT 3520. Restricted to Machine Learning Analyst.


This course is delivered in a classroom setting with face-to-face instruction at regularly scheduled times.
Term Duration Schedule Class Section Open Studies

Edmonton
Fall Sep. 1 - Dec. 11, 2026 Mon: 09:30 am - 12:30 pm
 41225 A01 No
Winter Jan. 4 - Apr. 16, 2027 Mon: 02:00 - 05:00 pm
 11241 A01 No
For each course session or class time, you can choose to attend in person, online in real-time or watch a previously recorded session.
Term Duration Schedule Class Section Open Studies

Edmonton
Fall Sep. 1 - Dec. 11, 2026 Mon: 09:30 am - 12:30 pm
 41226 H01 No
Winter Jan. 4 - Apr. 16, 2027 Mon: 02:00 - 05:00 pm
 11242 H01 No

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To take this course apply to our Machine Learning Analyst program. Read our application guide and apply now.

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