The professional masters in machine learning combines specialized coursework, with emphasis on Mila artificial intelligence classes, and work experience in the artificial intelligence industry.
Applicants should consult Mila recruitment procedure, indicating they are applying for the industrial masters. Once accepted, they can apply to the relevant department, and Mila will take care of finding internship and guiding the students during their work.
6390 Fondements de l’apprentissage machine
6758 Science des données
6135 Apprentissage de représentations
6759 Projets avancés en apprentissage automatique
Note that 6390, 6758 and 6135 are taught in English.
Expected knowledge to succeed in the program
Sufficient knowledge in computer science and mathematics is expected to enter the program (and is one of the criteria evaluated by the admission committee). In particular, the program builds upon the syllabus of the following courses (or their equivalent), which should be mastered by students of the program:
- MAT1400 – Calcul 1 (calculus)
- MAT1600 – Algèbre linéaire 1 (linear algebra)
- MAT1978 – Probabilités et statistique (probability and statistics)
- IFT2015 – Structures de données (data structures)
- IFT2125 – Introduction à l’algorithmique (algorithms)
If sufficient knowledge of these topics is not satisfied, the department may impose additional courses (note that these additional courses are taught in French).
To get a clear idea of the prerequisites needed for the program, we invite you to consult the prerequisites page for the course IFT 6390 – Fondements de l’Apprentissage Machine, which is one of the first courses of the program.
It is also assumed that students have some proficiency in programming (the equivalent of the content of the course IFT1015).
- IFT 6757 – Autonomous Vehicules
- IFT 6085 – Advanced Structured Prediction
- IFT 6135 – Learning representations
- IFT 6266 – Learning algorithms
- IFT 6285 – Natural Language Processing
- IFT 6269 – Probabilistic graphical models and learning
- IFT 6390 – Fondamentals of Machine Learning
- INF 6953H (Poly) – Deep Learning
- INF 8225 (Poly) – Probabilistic techniques and learning
- INF 8702 (Poly) – Advanced Computer Graphics
- MTH6404 (Poly) – Integer Programming
- 6-602-07 (HEC)- Applied multidimensional analysis
- 80-629-17A (HEC) – Machine Learning for Large-Scale Data Analysis & Decision Making
- COMP 550 (McGill)- Natural Language Processing
- COMP 551 (McGill) – Applied Machine Learning
- COMP 652 (McGill) – Machine Learning
- COMP 767 (McGill) – Advanced Topics: Reinforcement learning
Université de Montréal: Masters in Computer Science (internship option)
The program starts with 6 graduate level classes. This is followed by a 6 months internship in industry or in an academic laboratory. Students are supervised by a professor and a staff member from Mila research and development team, who provide daily guidance and linkage with industrial partners.