Business & Alumni > Events > Sobey Events Listing > Machine Learning Summer School

Machine Learning Summer School

Date: Monday, July 20, 2020

Research Portal on Machine Learning for Social and Health Policies

A project created in a partnership between Saint Mary's University and Dalhousie University, presents 


Register and learn more


Dr. Yigit Aydede, Economics, Sobey School of Business; and Dr. Mutlu Yuksel, Economics, Dalhousie University 

Machine learning is programming computers to build predictive models for making inference from samples for accurate out-of-sample predictions and decisions. As an applied economist, or researcher in a related field, if you feel that you need to learn these contemporary machine learning methods, implement them into your research, or looking for articles applied these methods to incorporate into the courses that you are teaching, this course is for you. 


The course will follow the book, Statistical Learning by Machines in R (forthcoming - 2020). The online version of the related notes will be provided to the participants. Data sets and R code will be available through a supporting website. The first sessions will start at 10am. Following the breaks between 12:30pm and 2:00pm, the second sessions will start at 2pm and end at 4:30pm. For those who would like additional training in R, there will be R Labs the first three day between 5:30pm and 7pm. All times are Atlantic Time zone.

July 20, Monday

Session 1 (10am - 12:30pm): Causal vs. Predictive Models, Translation of Concepts Used in Machine Learning for Social Scientists

Session 2 (2pm - 4:30pm): Overfitting, Model Selection, and Variance-Bias Trade-Off.

R-Session (5:30pm - 7pm): Additional training in R - voluntary

July 21, Tuesday

Session 1 (10am- 12:30pm): Regressions and Linear Classifiers, Nonparameteric Models, kNN.

Session 2 (2pm -4:30pm): Grid Search with Cross-Validation and Bootstrapping.

R-Session (5:30pm - 7pm): Additional training in R - voluntary

July 22, Wednesday

Session 1 (10am- 12:30pm): Classification and Regression Trees, Random Forests, Boosting, Bagging.

Session 2 (2pm -4:30pm): Penalized Regression Models: Lasso, Ridge, Elastics Net, and Adaptive Lasso.

R-Session (5:30pm - 7pm): Training in Data Revisions and Now-casting of macroeconomics series by Andrea Guisto,PhD

July 23: Presentations of Speakers


  • 10am - 10:55am : Juri Marcucci: Machine Learning in Macroeconomics
  • 11am - 11:55am : Arthur Charpentier: Machine Learning in Actuarial Science & Insurance
  • 12pm - 12:55pm : Arthur Spirling : Machine Learning in Political Science
  • 1pm - 1:55pm : Kathy Baylis: Machine Learning in Agricultural Economics
  • 2pm - 2:55pm : Stephan Wager : Machine Learning in Causal Inference
July 25: Presentations of Speakers
  • 10am - 10:55am : Stan Matwin : Machine Learning and Data Privacy
  • 11am - 11:55am : Mehmet Caner : Machine Learning in Econometrics
  • 12pm - 12:55pm : Anders Bredahl Kock : Machine Learning in Model Selection
  • 1pm - 1:55pm : Dario Sansone: Machine Learning in Education and Development Economics

Return to Event List