University of Michigan
Introduction to Machine Learning in Sports Analytics
University of Michigan

Introduction to Machine Learning in Sports Analytics

4,003 already enrolled

Gain insight into a topic and learn the fundamentals.
4.7

(19 reviews)

Intermediate level

Recommended experience

12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.7

(19 reviews)

Intermediate level

Recommended experience

12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Gain an understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

Details to know

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Assessments

4 quizzes

Taught in English

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This course is part of the Sports Performance Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

This week will introduce the concept of machine learning and describe the four major areas of places it can be used in sports analytics. The machine learning pipeline will be discussed, as well as some common issues one runs into when using machine learning for sports analytics.

What's included

7 videos3 readings1 quiz1 ungraded lab

In this week students will learn how Support Vector Machines (SVM) work, and will experience these models when looking at both baseball and wearable data. Coming out of the week students will have experience building SVMs with real data and will be able to apply them to problems of their own.

What's included

4 videos2 readings1 quiz

This week will focus on interpretable methods for machine learning with a particular focus on decision trees. Students will learn how these models work in general, and see special uses of decision trees in combination with regression methods. In this week students will come to better understand how the python sklearn toolkit can be used for a breadth of supervised learning tasks.

What's included

4 videos2 readings1 quiz

In this week of the course students will learn how many different models can be used together through ensembles, including the random forest method as a common use, as well as more general methods available in sklearn such as stacking and bagging. By the end of this week students will have a broad understanding of how methods such as SVMs, decision trees, and logistic regression can be used together to solve a problem with increasing performance.

What's included

5 videos3 readings1 quiz

Instructor

Christopher Brooks
15 Courses877,972 learners

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4.7

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5

Reviewed on May 6, 2023

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5

Reviewed on Dec 4, 2022

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Reviewed on Oct 24, 2022

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