Date Posted: 07/26/2024
Req ID: 38931
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 (2 positions available)

Course title and code: AI in Finance  - APS1052

Course description: In this course we’ll give an overview of several applications of machine learning to stock market forecasting (including high frequency trading), beginning with regressions, two “shallow” machine learning models (Support Vector Machines and basic Neural Networks) and ending with a deep learning model (Long Short Term Memory Networks). Each model is discussed in detail as to what input variables and what architecture is used (rationale), how the model’s learning progress is evaluated and how machine learning scientists and stock market traders evaluate the model’s final performance, so that by the end of the course, the students should be able to identify the main features of a machine learning model for stock market forecasting and to evaluate if it is likely to be useful and if it is structured efficiently in terms of inputs and outputs.

The participant should be familiar with the foundations of statistics, the basics of logistic regressions (desirable) and basic linear algebra (desirable); however, since our course intends to be self-contained, we will provide a review of these concepts as needed. As all the examples of our course come from finance, some familiarity with the Capital Markets and the basic financial concepts is required. A basic knowledge of Python or some other programming language (MatLab, R) is needed, even though the objective of the course is not to learn how to program (shallow & deep) machine learning models from scratch, but rather, to understand how they work and to learn how to adapt them to the particular needs of the user and to optimize their application to stock market forecasting. The math. foundations of the basic machine learning models (regressions, neural networks & support vector machines) will be discussed and followed by a panoramic view of the inputs that are most likely to provide valuable information for stock market forecasting. Standard benchmarking methods used in the industry will be also covered. Subsequently, a number of basic –already programmed– models will be discussed in detail and their performance evaluated.

Estimated Enrolment: Approximately 50

Estimated TA support: TBA

Class schedule: One 3-hour lecture per week.

Sessional date of appointment: Fall Session Sept - Dec 2024.

Salary: Minimum level of pay is $4,728.94 each (50% of Sessional Lecturer I as co-instructor), which includes vacation pay, and may increase depending on applicant’s level of experience and suitability for the position.

Qualifications: Expertise in machine learning, statistics, mathematics, and programming languages. Experience as instructor at the undergraduate or graduate level as well as the ability to effectively communicate and explain concepts clearly. Applicants should have a strong record of presenting lectures. The applicant must be able to lecture in a clear voice.

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 for delivery; delivery of lectures; possible supervision of Teaching Assistants; setting and marking of projects, tests and exams; evaluation of final grades; contact with students.

To indicate interest in this position, please and send an updated CV and a completed application CUPE UNIT 3 form, downloaded from: https://gradstudies.engineering.utoronto.ca/files/2022/08/UNIT-3-Application-Form.pdf to  gradstudies@engineering.utoronto.ca

 David Duong, Graduate Engineering Affairs, Faculty of Applied Science and Engineering, University of Toronto

44 St. George Street, Toronto, Ontario M5S 2E4

Email: gradstudies@engineering.utoronto.ca

Closing Date: 08/11/2024, 11:59PM EDT
**

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.