Sessional Lecturer - CSC2515HS - Introduction to Machine Learning

Date Posted: 10/28/2026
Req ID: 50812
Faculty/Division: Faculty of Arts & Science
Department: Dept of Computer Science
Campus: St. George (Downtown Toronto)

Description:

Course number and title: CSC2515HS - Introduction to Machine Learning

Please note, this position is a 0.5 FCE appointment.

Course description:

Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour manually. This course introduces the main concepts and ideas in ML and provides an overview of many commonly used machine learning algorithms. It also serves as a foundation for more advanced ML courses.

The students will learn about ML problems (supervised, unsupervised, and reinforcement learning), models (linear and nonlinear, including neural networks), loss functions (squared error, cross entropy, hinge, exponential), bias and variance trade-off, ensemble methods (bagging and boosting), optimization techniques in ML, probabilistic viewpoint of ML, etc.

Reference: https://web.cs.toronto.edu/graduate/course-descriptions#csc2515s

Estimated course enrolment: 50 students per section

Estimated TA support: support: 90 hours of TA support

Class schedule:

Wednesday 11:00-13:00

*Please note, the delivery method for this course is currently in-person. Please note that, in keeping with current circumstances, the section delivery method may change as determined by the Faculty or the Department.

Sessional dates of appointment: January 1, 2027 – April 30, 2027

Salary:

Sessional Lecturer I = $14,125.85;
Sessional Lecturer I - Long Term = $15,794.49;
Sessional Lecturer II = $15,794.49;
Sessional Lecturer II - Long Term = $16,906.58;
Sessional Lecturer III = $16,906.58;
Sessional Lecturer III - Long Term = $17,440.58;

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.

Minimum qualifications:

• Doctoral degree in Computer Science or closely related field required.
• Demonstrated expertise in topic area of the course required.
• Strong organizational, interpersonal, and communication skills required.
• Teaching experience at the university level or equivalent industry level required.

Preferred qualifications:

• Previous experience teaching undergraduate courses in the field of Computer Science preferred.
• Demonstrated evidence of excellence in teaching preferred.
  

Description of duties:

• Preparing and delivering the lectures in-person on campus as scheduled.
• Handling course administration including: maintaining the course website on quercus; developing marking schemes/syllabus; planning tutorial content (when applicable); developing course assessments including assignments, projects, quizzes, tests, and final assessments.
• Providing appropriate contact time outside of class to students, through office hours, email, the course website and/or the course bulletin board.
• Preparing the breakdown of hours for TA duties in the course and supervising the TAs.
• Ensuring that tutorials and/or labs are delivered appropriately by the TAs as applicable.
• Managing the grading for the course, which is largely done by the TAs, and carrying out any grading not handled by the TAs.
• Invigilating term tests and the final exam when applicable.
• Managing the grades, including the timely completion and release of grades and feedback to students throughout the term; submitting final course grades (due May 8, 2026).
• This course will have a faculty supervisor; the successful candidate will be expected to liason with the faculty supervisor throughout the term with regular meetings to discuss topics such as: preparing course materials for the syllabus, lecture topics, and course assessments.

While there is a lot of room for creativity in course delivery, instructors will be expected to follow the basic content and style used by the faculty members who normally teach the course, and must get approval from these faculty members or from the Associate Chair for any substantial changes to the course content or assessment methods. Instructors will also be expected to consult with the department’s Teaching Support group when creating the course syllabus and course assessments (tests, assignments, projects, and final exam)

Application instructions: All individuals interested in this position must submit their application by using the following application form. The direct link is: https://forms.cloud.microsoft/r/CA7ytdCxri. This includes submitting an updated Curriculum Vitae and the CUPE 3902 Unit 3 application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form. If you have any questions, please email: [email protected].

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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 email: [email protected].

Closing Date: 11/16/2026, 11:59PM EDT
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This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.

It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.

Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.