AML-TN students are enabled to take their academic experience further. Actively mentored and guided through a bespoke Individual Development Plan (IDP), students set goals, track deliverables, and assess progress to better prepare themselves for the next stages in their career. Our aim is a well-rounded training experience that prepares graduates to thrive in research, industry, and other professional paths.
While completing the core UBC curriculum contributes to development, AML-TN curates a wide range of technical and professional training opportunities designed to prepare trainees for impactful careers in academia, industry, and beyond. Explore our suggested opportunities in the tabs below.
AML-TN’s training standards are not intended to be a significant burden on top of a rigorous graduate degree. Keeping in mind that your primary responsibility is your academics, each of these deliverables is designed to add a manageable workload while adding significant value.
Professional Skills
Professional skills development is an integral part of the program, with master’s students targeting at least 40 hours and PhD students at least 80 hours of training over the course of their degree. These activities span communication, leadership, ethics, project management, and other topics that strengthen a student’s ability to work effectively and responsibly. The time commitment —about 20 hours per year—and is designed to enhance, not distract from, research progress.
Technical Skills
Technical training ensures that students develop deep expertise in machine learning while gaining hands-on experience with applied tools and practices. With 100 hours required for master’s students and 150 hours for PhDs, this training builds the confidence to translate ML concepts into impactful solutions and prepares trainees for collaboration and real-world implementation.
Individual Development Plan (IDP)
The IDP serves as a personal roadmap for each student, balancing core coursework, professional skills development, and technical training. It encourages intentional planning and reflection, helping students connect their academic work with longer-term aspirations. In addition to the training targets mentioned above, AML-TN graduate students are expected to lead reading group sessions at least once per year.
Recommended Opportunities
Professional Training
The workshops and seminars mentioned here satisfy the professional skills requirement. Students are encouraged to select the activities that best match their goals, from communication and teaching development to leadership, ethics, and research management. Students are also free to leverage opportunities not on this list.
UBC Workshops
| Training Opportunity | Key Areas | IDP Credit |
|---|---|---|
| UBC Graduate Pathways to Success Events Calendar | Graduate school success, self management, professional effectiveness, career building, leadership | Estimated 1-2 hour time commitment per session |
| UBC Cloud Innovation Centre (CIC) Events Calendar | Amazon Web Services (AWS) functionality, community health and wellness | Estimated 1-3 hour time commitment per session |
| UBC Career Centre Events Calendar | Applying for jobs, resume & LinkedIn tips | Estimated 1-3 hour time commitment per session |
| UBC Centre for Writing and Scholarly Communication Events Calendar | Effective writing, research, and presentation skills | Estimated 1-3 hour time commitment per session |
| UBC Equity & Inclusion Office Events Calendar | Inclusive work environment, positive space workshop, IBPOC women/gender diverse networking | Estimated 1-3 hour time commitment per session |
| UBC Wellbeing Events Calendar | Work/life balance, community building with respect, wellness | Estimated 1-3 hour time commitment per session |
| UBC Centre for Teaching, Learning, and Technology Events Calendar | Professional development related to teaching and learning endeavors | Estimated 1-24 hour time commitment per course |
Research Events & External Training
| Training Opportunity | Key Areas | IDP Credit |
|---|---|---|
| Centre for Artificial Intelligence Decision-making and Action (CAIDA) Events Calendar | Estimated 1-4 hour time commitment per session | |
| TrustML Research Cluster Events Calendar | Trustworthiness of machine learning based systems | Estimated 3-9 hour time commitment per session |
| Entrepreneurship@UBC Events Calendar | Entrepreneurship, leadership, enterprise development | Estimated 2-5 hour time commitment per session |
| Mitacs Training | Networking skills, project & time management, reconciliation and EDI, communication skills, career planning, R&D management, leadership skills, writing and presentation skills | Estimated 1-3 hour time commitment per session |
Technical Training
Here you’ll find courses and resources to meet the technical training requirement, including advanced ML methods, programming workshops, and reproducibility-focused sessions designed to strengthen your technical foundation.
UBC Machine Learning Courses
Undergraduate Courses
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Problem-solving and planning; state/action models and graph searching. Natural language understanding Computational vision. Applications of artificial intelligence. [3-0-0] Prerequisite: CPSC 221.
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Application of machine learning tools, with an emphasis on solving practical problems. Data cleaning, feature extraction, supervised and unsupervised machine learning, reproducible workflows, and communicating results. [3-0-1] Prerequisite: Either (a) one of CPSC_V 203, CPSC_V 210, CPEN_V 221, DSCI_V 221 or (b) MATH_V 210 and one of CPSC_V 107, CPSC_V 110.
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Foundations and concepts of data science and machine learning with applications to engineering problems. [3-0-2] Prerequisite: One of MATH_V 152, MATH_V 221 and one of MATH_V 318, MATH_V 302, STAT_V 302, STAT_V 321, ELEC_V 321 and one of CPEN_V 221, CPEN_V 223, CPSC_V 259. Credit cannot be obtained for both CPEN_V 355 and CPSC_V 340.
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Principles and techniques underlying the design, implementation and evaluation of intelligent computational systems. Applications of artificial intelligence to natural language understanding, image understanding and computer-based expert and advisor systems. Advanced symbolic programming methodology. [3-0-0] Prerequisite: CPSC 322.
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Advanced machine learning techniques focusing on probabilistic models. Deep learning and differentiable programming, exponential families and Bayesian inference, probabilistic graphical models and other generative models, Monte Carlo and variational inference methods. [3-0-1] Prerequisite: All of CPSC 320, CPSC 340.
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Artificial intelligence, machine learning techniques, Deep learning, Python libraries for machine learning, Basic signal processing techniques, Data Acquisition, Applications in Manufacturing, Use of sound to evaluate operation of CNC machinery, Use of AE sensor to evaluate metal 3D printing. [3-0-1].
Graduate Courses
Additional Opportunities
| Training Opportunity | IDP Credit |
|---|---|
| UBC Advanced Research Computing (ARC) Events Calendar | Estimated 2-3 hour time commitment per session |
| Research Data Mining @ UBC Events Calendar | |
| SciNet @ UofT Events Calendar | Estimated 1-3 hour time commitment per session |
| Western Deans' Agreement Courses at Partnering Institutions | |
| CIFAR Deep Learning + Reinforcement Learning Summer School |
Reading Groups
Reading groups and seminar series offer an informal but highly valuable way to stay current with the latest research and exchange ideas with peers, while improving presentation and critical analysis skills.