Associate Professor of Information
Introduction to Machine Learning in Sports Analytics
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.
4 Modules
12 Hours
3 hrs per module (approx.)
Rating
About Introduction to Machine Learning in Sports Analytics
In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs). By the end of the course students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.
Skills You'll Gain
- Data Analysis
- Data Visualization
- Machine Learning
- Sports Analytics
- Statistical Analysis
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Introduction to Machine Learning in Sports Analytics, a course that applies supervised machine learning techniques to real-world sports data. Learners use Python and scikit-learn to explore classification and regression methods for predicting athletic performance and outcomes. This is the final course in the Sports Performance Analytics Specialization.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Machine Learning Concepts
- Ungraded Lab: JupyterLab
- Video: Introduction
- Reading: Help Us Learn More About You
- Reading: Course Syllabus
- Video: What is Machine Learning?
- Video: The Machine Learning Workflow
- Video: Our First Model: NHL Game Outcomes
- Video: Building the Logistic Regression Model
- Video: Considerations in Deploying The Model
- Video: Wrap Up
- Reading: Assignment 1 Programming Solution
- Graded: Assignment 1
Module 2: Support Vector Machines
- Video: Introduction to Support Vector Machines (SVMs)
- Video: Polynomial Support Vector Machines
- Video: Cross Validation
- Video: A Real World SVM Model: Boxing Punch Classification
- Reading: (Optional) - An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing
- Reading: Assignment 2 Programming Solution
- Graded: Assignment 2
Module 3: Decision Trees
- Video: Decision Trees
- Video: A Multiclass Tree Approach
- Video: Model Trees
- Video: Tuning and Inspecting Model Trees
- Reading: Assignment 3 Programming Solution
- Reading: UM Master of Applied Data Science (optional)
- Graded: Assignment 3
Module 4: Ensembles & Beyond
- Video: Ensembles
- Video: Additional Machine Learning Concepts
- Video: Baseball Hall of Fame Prediction
- Video: Baseball Hall of Fame Demonstration Part 1
- Video: Baseball Hall of Fame Demonstration Part 2
- Reading: Free Deepnote Notebook Service
- Reading: Putting Your Skills to the Test!
- Reading: Post Course Survey
- Graded: Assignment 4
Grading Policy
Learners must earn 80% or higher on all assessments. Each module contains one graded assignment worth 25% of your total grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
