[Sessional Lecturer] Foundations of Data Analytics and Machine Learning - APS1070

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 :