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Machine Learning - Online Course

A 4-Day Livestream Seminar Taught by

Kevin Grimm
Course Dates: Ask about upcoming dates
Schedule: All sessions are held live via Zoom. All times are ET (New York time).

10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm

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NOTE: This is an introductory seminar on machine learning. If you already have substantial knowledge of machine learning and experience with implementing it, you may want to check out our seminar on Advanced Machine Learning.

Machine learning has emerged as a major field of statistics and data analysis where the goal is to create reliable and flexible predictive models. This seminar offers a thorough introduction to machine learning methods. Topics covered include: cross-validation; multiple regression; basic variable selection methods; an overview of the R statistical framework; and advanced variable selection methods for regression analysis.

Starting May 16, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 

Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

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“Kevin did an excellent job of introducing the theoretical concepts of machine learning..."

“Kevin did an excellent job of introducing the theoretical concepts of machine learning, then visualizing them, and finally providing practice with the code. He has extensive knowledge of the academic application of machine learning. His Q&A sessions were extremely beneficial!”

Yufan (Frank) Lin

Cal Poly Pomona

"Kevin came across as a great teacher who understood how learning takes place."

“The material was well picked and thoughtfully delivered with good logical flow and building step-by-step onto the previous statement/conclusion. I liked that the course had a lot of hands-on examples. I appreciated the daily schedule of the course and how there were enough breaks to digest and practice the material. Kevin came across as a great teacher who understood how learning takes place. His humor and friendly personality made him very approachable.” 

Emese O'Donnell

JHU/SOM 

“The course was excellent..."

“The course was excellent because it gave me the opportunity to learn new and advanced tools for making complex concepts easier to understand. In the beginning, I thought it was challenging to learn these new tools, but by the end of the course, I was eager to learn more about these new concepts. The examples provided were enough to get a good idea of potential applications.” 

Delfino Vargas

National Autonomous University of Mexico 

"Would highly recommend the course to anyone interested in learning the basics of ML."

Excellent didactics, great mix of coding/theory with excellent practical examples. Kevin builds up towards complex models from simple ones in a very elegant way. Would highly recommend the course to anyone interested in learning the basics of ML. 

Andre Amaral

University of Toronto

“I appreciated that the complex topics were explained in simple words..."

“I appreciated that the complex topics were explained in simple words, as well as the focus on ‘what it means and how it works’ from a wider perspective rather than digging into technical nitty-gritties.”

Aliaksei Laureshyn

Lund University

"Very attentive to our needs and difficulties..."

“Kevin was an excellent tutor! Very attentive to our needs and difficulties, very patient and always helpful. Topics were all very interesting. Examples were worked through in detail. Recommended for both beginners and advanced learners!”

Panagiotis Ferentinos

National and Kapodistrian University of Athens

“I can’t tell you how much I enjoyed the course!"

“I can’t tell you how much I enjoyed the course! Dr. Grimm did a great job. He is superb! He covered the machine learning algorithms and approaches thoroughly, and taught in such a way that it was easy to understand. Another amazing fact is that he not only provided the detailed R code and but also went through the code during the lectures and labs. The lectures were very engaging. I attended this workshop for my own interest and for my research program. I thoroughly enjoyed!”

Saumen Mandal

University of Manitoba

"His expertise in latent variable modeling also allows for fruitful cross-disciplinary exchanges."

“The course addresses the most important supervised machine learning algorithms and approaches in a clear and comprehensive way. Professor Kevin Grimm provides excellent and clear explanations, as well as a plethora of useful material. His expertise in latent variable modeling also allows for fruitful cross-disciplinary exchanges. I highly recommend this course to anyone interested in deepening his/her expertise in this area.”

Enrico Perinelli

University of Trento