MLportal is one of the recipients of Nova Scotia COVID-19 Health Research Coalition Funding
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. In this course, participants will learn the fundamentals of machine learning and their implementation, recalling the fundamental statistical concepts at the heart of modern learning techniques. Participants will learn the differences between causal and predictive analyses and their relative merits, as well as their use in applied social sciences.
The course will combine both real data and theoretical background to enable researchers to gain practical experience in analysing a wide variety of data and econometric problems. It will also discuss how contemporary approaches in applied econometrics can be used to answer important questions in Economics. Participants will be provided a wealth of research papers and sources which apply the techniques being taught. Applications covered during the course will include the fields of labour, development, industrial organisation, health, macro economics and finance.
By the end of the course, participants will be able to understand various concepts intensively used in the machine learning literature such as cross-validation, bootstrapping, boosting, bagging, optimization routines, and identify common techniques, such as CART, Random Forest, MARS, GAM, Lasso, most suitable for their research questions and data and their natural extensions to causal inference with observational data.
The participants will be expected to be familiar with concepts and practices of traditional multivariate regression analysis, and related estimation and causal methods.
All examples will be given in R or Stata, so you should also have a working knowledge of these software (which can be replaced by an advanced knowledge of another programming language, such as Python, to adapt the codes and exercises on your own). Participants are invited to bring their own laptop with R-Studio and Stata installed on it.
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 lunch 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 Eastern Time.
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
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
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): Additional training in R - voluntary
Anders Bredahl Kock : Associate Professor, University of Oxford : ML in Time Series and Finance
Arthur Charpentier: Professor, Université du Québec à Montréal : ML in Actuarial Science & Insurance
Arthur Spirling : Professor of Politics and Director of The Center for Data Science , NYU : ML in Political Science
Juri Marcucci: Economist at the Research Department , Bank of Italy: ML in Macroeconomics
Kathy Baylis: Professor, Agricultural and Consumer Economics, University of Illinois: ML in Agricultural Economics
Mehmet Caner : Thurman-Raytheon Distinguished Professor of Economics, NC State University: ML in Econometrics
Stefan Wager : Assistant Professor of O.I.T. and Statistics, Stanford GSB: ML in Casual Inference
Tentative:
1. Advanced Ph.D.s, Postdocs and temporary university researchers: $200 CAD.
2. Faculty and researchers: $300 CAD.
3. Others: $600 CAD.
Fee includes the course related documents.
Application deadline: June 5th, 2020.
Fees Payment deadline: June 15th, 2020.
The Summer School is conditional to the recruitment of a minimum of 10 participants.
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