Machine Learning

A 2-Day Seminar Taught by Kevin Grimm, Ph.D.

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To see a sample of the course materials, click here.


Machine learning has emerged as a major field of statistics and data analysis where the goal is to create reliable and flexible predictive models. These methods have gained much attention for analyzing large datasets that may be composed of several hundred variables and many thousands (perhaps millions) of participants. In these situations, machine learning algorithms attempt to identify key variables needed in the predictive model, and several techniques search for nonlinear associations and interactive effects.

While machine learning techniques have been most attractive for large datasets, these same techniques can be useful in smaller datasets for the same reasons–to create simpler and more reliable predictive models, and to search for nonlinear and interactive effects. These techniques are also a natural follow-up to standard hypothesis-driven statistical analyses (e.g., multiple regression) to search for additional important patterns in the data.

The first day starts with an overview of machine learning and continues with an introduction to the basic techniques. Topics for day 1 include cross-validation, multiple regression, and basic variable selection methods, as well as an overview of the R statistical framework. The second day focuses on advanced variable selection methods for regression analysis. Topics include multivariate adaptive regression splines, lasso regression, classification and regression trees, bagging, and random forests. Throughout the course, participants gain experience with these methods through hands-on exercises.


Computing

This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms


Who should attend? 

If you have a desire to learn how to effectively explore your data and have a strong statistical background in regression, this course is for you. You should have a good working knowledge of the principles and practice of multiple regression. It is also helpful to have familiarity with the R programming language.

There are a number of excellent introductory books to R as well as a collection of online tutorials for people who are unfamiliar with R (e.g., Tutorials Point).


LOCAtions, Format, And Materials 

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Jamaica Bay Inn, 4175 Admiralty Way, Marina Del Rey, CA 90292. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 


Registration and lodging

The fee of $995.00 includes all seminar materials. The early registration fee of $895.00 is available until May 27. 

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Jamaica Bay Inn, 4175 Admiralty Way, Marina Del Rey, CA 90292, where the seminar takes place, at a special rate of $219 per night for city view rooms or $239 per night for ocean view rooms. In order to make reservations, call 310-823-5333 during business hours and identify yourself as part of the Statistical Horizons group. For guaranteed rate and availability, you must reserve your room no later than Monday, May 27, 2019. 

We also recommend going directly to the hotel’s website or checking other online hotel sites. Pricing varies and you may be able to secure a better rate. 


SEMINAR OUTLINE

1. Introduction to Machine Learning
     a. Introduction to machine learning
     b. Introduction to R
     c. Single predictor regression models & cross-validation
     d. Multiple regression
     e. Best subsets regression & forward selection

2. Advanced Variable Selection
     a. Multivariate adaptive regression splines & lasso regression
     b. Review of logistic regression & decision theory
     c. Classification & regression trees
     d. Bagging trees & random forests


RECENT COMMENTS FROM PARTICIPANTS

“This workshop was extremely well organized. The instructor was very knowledgeable on the topic.”
  Soyang Kwon, Northwestern University

“If you have a reasonably broad knowledge of modeling and measurement and want to extend this to include machine learning, this course definitely takes you there. Helped solidify my understanding and importance of machine learning.”
  John Fava, Vanguard

“The instructor was very knowledgeable and generally very good at conveying complex concepts. His presentations were detailed and well-organized. He was generally very open to answering all questions.”
  Wilson Vincent, University of California, San Francisco

“Wonderful course. Describing machine learning from a statistical perspective. Dr. Grimm is a good speaker and lecturer. He explained everything clearly. It would be perfect for anyone with some statistical and R foundation.”
  Jie Wang, University of California, Los Angeles

“This was an excellent course. The instructor presents the material clearly and with several examples to demonstrate the methods. I feel able to apply these methods in my work.”
  Michael Monuteaux, Boston Children’s Hospital

“Dr. Grimm is a very good instructor who clearly communicated and explained the material. His mastery of the material was evident. Good level of enthusiasm.”
  Mark Boye, Eli Lilly and Company, Inc.