APTPUO-summer -MIAZ

Position: APTPUO-summer 2026-MIA5100 Z

Description

:

This course provides an in-depth exploration of the foundational topics in Machine Learning (ML) and Artificial Intelligence (AI), encompassing a broad range of concepts, algorithms, frameworks, methodologies, and practical applications. Topics will ranges from areas such as feature engineering, supervised and unsupervised learning, deep learning, natural language processing, and model deployment using state-of-art techniques. As part of this course, students are expected to develop the skills and knowledge necessary to design, implement, evaluate ML models, and deploy ML models effectively.

Application areas will emphasize real-world contexts such as arts, business, social sciences, and law domains. The course format includes lectures, discussions, and lab sessions to facilitate comprehensive learning.

Posting limited to:

Professeur à temps-partiel régulier / Regular Part-Time Professor

Date Posted (YYYY/MM/DD):

2026/01/26

Applications must be received
BEFORE (YYYY/MM/DD):

2026/02/27

Expected Enrolment:

40

Approval date:

2026/01/26

Number of credits:

3

Work Hours:

39

Hourly Rate:

Enseignement / Teaching: $239.47 )

The academic year starts on September 1 and ends on August 31.

These rates do not included vacation pay nor statutory pay.

These rates will be applied until a new collective agreement is ratified. Retro will be paid after the ratification.

Course type:

B

Posting type:

Régulier / Regular

Language of instruction:

Anglais | English

Competence in second language:

Active

Course

Schedule:

Mardi | Tuesday 19:00-22:00 - -

Requirements:

Ph.D. in AI, Machine Learning, DTI, Computer Science, Statistics, Mathematics, Engineering, or a related field.

Demonstrated expertise in AI/Machine Learning, with a general focus on areas such as Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), and model deployment, including applications in real-world scenarios related to law, business, social sciences, and arts.

Teaching experience at the graduate and/or undergraduate level, preferably in AI/Machine Learning or related fields.

Hands-on experience with industry tools and technologies commonly used for developing and deploying Machine Learning, Deep Learning, and NLP algorithms, such as Python, Tensor Flow, PyTorch, Scikit-Learn, Transformers, NLTK, Spa Cy, Streamlit, Flask, etc.

Preference will be given to candidates with experience in project-based learning or experiential learning approaches in AI/Machine Learning.

Additional Information and/or Comments:

An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience.