[Sessional Lecturer] Introduction to Reinforcement Learning - APS1080

Date Posted: 03/26/2024
Req ID: 36680
Faculty/Division: Faculty of Applied Science & Engineering
Department: APSC: Ofc of the Dean - Faculty General
Campus: St. George (Downtown Toronto)

 

Description:

Position: Sessional Lecturer I (1 positions available)

 

Course title and code: Introduction to Reinforcement Learning – APS1080

 

Course description: Reinforcement Learning (RL) is a systems-level Artificial Intelligence toolset; this course will provide the student with both a solid theoretical foundation and a strong practical understanding of the subject.

RL enables autonomous agents to cope with poorly-characterized, novel environments by exploring the environment to gain knowledge about it, and to exploit this knowledge of the environment to act in a goal-directed manner.  Although RL is positioned as one of three facets of Machine Learning, RL has far broader scope than the narrower tools of supervised and unsupervised learning. RL, being founded on agent design, has the goal of developing artificial intelligence schemes that can endow an agent with autonomy. This introduction, thus, will be presented within the motivating context of an overall AI system.  There are three foundational RL tools we will cover (dynamic programming, Monte Carlo, Temporal-Difference Learning); we will also show how hybridizations of these foundational tools are employed to create production schemes. The student should leave the course with the ability to practically apply this AI toolset to novel problems.

 

Estimated Enrolment: Approximately 50 students

 

Estimated TA support: TBA

 

Class schedule: One 3-hour lecture per week.

 

Sessional date of appointment: Summer May-August 2024

 

Salary: Minimum level of pay is $9,457.89 (Sessional Lecturer I), which includes vacation pay, and may increase depending on applicant’s level of experience and suitability for the position.

 

Qualifications: PhD with a focus on AI or related fields; experience with Reinforcement Learning, Control Theory, Agent Design, and Artificial Intelligence, in both a practical and theoretical sense.

 

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.

 

Brief description of duties: Duties include: preparation of lectures and course materials; delivery of lectures; possible supervision of Teaching Assistants; setting and marking of projects, tests and exams; evaluation of final grades; contact with students. Online Delivery.

 

To indicate interest in this position, please complete the CUPE UNIT 3 application form, downloaded from:

https://gradstudies.engineering.utoronto.ca/files/2022/08/UNIT-3-Application-Form.pdf and submit to  gradstudies@engineering.utoronto.ca

 

Office of the Vice Dean Graduate Studies, Faculty of Applied Science and Engineering, University of Toronto

44 St. George Street, Toronto, Ontario M5S 2E4, Email: gradstudies@engineering.utoronto.ca

 

 

 

Closing Date: 04/07/2024, 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.