[sessional Lecturer] Foundations of Data Analytics

2 months ago

Toronto, Canada University of Toronto Full time

**Date Posted**:10/10/2024
**Req ID**: 40113
**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 position available)

**Course title and code**: Foundations of Data Analytics and Machine Learning - APS1070

**Course description**: (1) Python programming (basic structures - tuples, lists, sets, dictionaries, Pythonic programming style, e.g., list comprehensions, common packages - numpy, scipy, matplotlib, pandas, Jupyter/IPython notebooks, OOP design & polymorphism and how to make effective use of it)

(2) Probability and statistics (basic distributions, expectations and Monte Carlo approximations, importance sampling, change of variables / Jacobian, ANOVA / confidence intervals).

(3) Matrix representations and fundamental linear algebra operations (e.g., quadratic form and multivariate Gaussians, trace, inverse, SVD, matrix derivatives): students should become familiar representing and manipulating expressions in matrix form.

(4) Basic algorithms and data structures (sorting and array search, graphs and trees)

(5) Discrete math (basic combinatorics, basic discrete optimization, e.g., weighted set cover)

(6) Continuous optimization (gradient descent and variants, convexity)

(7) Constrained optimization (linear programming, mixed integer linear programming): focus on problem formulation, use Gurobi for hands-on exercises

**Estimated Enrolment**: Approximately 100 students

**Estimated TA support**: TBA

**Class schedule**: One 3-hour lecture per week.

**Sessional date of appointment**: Winter Session 2025

**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**: Applicants must have experience in the use of artificial intelligence and machine learning in engineering. The applicant must have advanced Python programming skills and advanced knowledge of probability and statistics, matrix representations and fundamental linear algebra operations. In addition, the applicant must be familiar with basic algorithms and data structures, discrete math and continuous optimization (gradient descent and variants, convexity). The applicant must have experience in teaching mathematics or coding at the undergraduate or graduate levels. The applicant must be able to lecture in a clear voice, and explain concepts clearly.

Must be able to teach the following schedule: Lec01: Friday 9:00-10:30 and 2-330pm, Lec02: Friday 10:30-12:00 and 330pm-5pm

**_

**Brief description of duties**: As course coordinator, Prepares course schedule/syllabus, selects/organizes lab TAs, coordinates/supervises course lecturer and TAs, prepares/schedules midterM/Final (with course lecturer), and supervises midterm & final and marking - make sure everything runs smoothly.

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

**Closing Date**: 10/17/2024, 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._

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