Sessional Lecturer INF2179H

University of Toronto; Toronto; On-site; CAD 100,000 - 125,000; Part time

Job summary

A prominent educational institution in Toronto is seeking a Sessional Lecturer for the course INF2179H - Machine Learning with Applications in Python for the Summer Term 2026. The ideal candidate will preferably hold a completed or nearly completed PhD or a Master’s degree with significant professional experience related to the course content. Teaching experience is preferred as the role involves preparing course materials, delivering lectures, and developing course assessments. The position offers an opportunity to educate students in advancing machine learning methodologies.

Qualifications

• Preferably candidates will have a completed or nearly completed PhD degree in an area related to the course or a Master’s degree plus extensive professional experience in an area related to the course.
• Teaching experience is preferred.

Responsibilities

• Preparing course materials.
• Delivering course content including seminars, lectures, and labs.
• Developing and administering course assignments, tests & exams.
• Grading and holding regular office hours.

Skills

Teaching experience

Machine learning knowledge

Python proficiency

Education

Completed or nearly completed PhD in a related field

Master’s degree plus extensive professional experience

Job description

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Sessional Lecturer, INF2179H - Machine Learning with Applications in Python

University of Toronto
Faculty of Information

Sessional Lecturer

Summer Term 2026 – Session Y (May – August)

INF2179H – Machine Learning with Applications in Python

Course Description: Machine learning has recently become the dominant field in AI research and constitutes the main part of the tools applied in industry‑based AI positions. Business analysts, data scientists and AI engineers are required to know machine learning at different levels. The course will give a broad high‑level overview of state‑of‑the‑art machine learning methodologies. We shall focus on the application of these techniques to real‑world data using the most advanced tools available for Python. The techniques will include: linear regression, basic techniques for classification, advanced regression and classification methods, and unsupervised learning.

Estimate of TA Support: None anticipated. Estimate of 75 hours with enrollment of 36 or greater. Allocation of TA hours, if any, will be based on enrolment numbers.

Class Schedule: TBD. You are required to be located in geographical proximity to the applicable University premises in order to attend and perform your duties on University premises as of the Starting Date.

Sessional dates of appointment: May 1, 2026 – August 31, 2026

Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Qualifications: Preferably candidates will have a completed or nearly completed PhD degree in an area related to the course or a Master’s degree plus extensive professional experience in an area related to the course. Teaching experience is preferred.

Brief description of duties: Preparing course materials; delivering course content (e.g., seminars, lectures, and labs); developing and administering course assignments, tests & exams; grading; holding regular office hours.

Nafiseh Yazdian, Administrative Coordinator
Faculty of Information, 140 St. George Street, University of Toronto
[e-mail]
This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement. Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II and Sessional Lecturer III in accordance with Article 14:12.

Diversity Statement

The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission.
As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

Accessibility Statement

The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission.
The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities.
If you require any accommodations at any point during the application and hiring process, please contact [e-mail].