APM496H
Instructor: Prof. Luis Seco, Director Master in Mathematical Finance (MMF),
Professor of Mathematics.
Open for third and fourth year undergraduate
students with a good science or engineering background.
The course will consist of three different components:
1. A general
discussion on Data Science, ML/AI taught by Prof. Seco.
2. A course on
optimization and ML/AI by Prof. Roy Kwon
3. Satellite
discussions by subject matter experts:
a. Nicholas Hoell, MMF and Data Scientist at Deloitte
b. Rahul Raina,
Microsoft
c. Prof. Lennon Li,
School of Public Health, University of Toronto
4. Laboratory
assignments, in Python, by University of Toronto Teaching Assistants:
a. Yichao Chen
b. Amin Sammara
Data
Analysis
Exploratory Data Analysis
Model Formulation
Goodness of Fit Testing
Standard and Nonstandard Statistical Analysis
Linear and non linear Regression
Analysis of Variance
Timeseries Analysis
Machine
Learning
Feasibility of learning
Measures of Fit and Lift
Logistic Regression
Neural Networks
Support Vector Machines
Boosting, Decision Trees
Optimization
Linear and quadratic programming
Applications
Inference by Prof. Lennon Li
· Part I: Introduction to biostatistics
Lab and
tutorials
Introduction to Programming for Data Science
Prof. Seco’s
Presentations:


Presentation
by Rahul Raina, Microsoft.
Prof. Roy Kwon Lecture
notes
March
9, Linear Algebra review
March 18:
·
Hyperparameter
Cros Validation
·
One
Dimensional Newton’s method
Course evaluation
The course
will have two requirements that will be the basis for students
marks:
1. Inclass participation (20%)
2. Assignments (40%)
3. Final Presentation