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 o
