Course Description

The field of Machine Learning encompasses a broad range of methods for extrapolating from data, from finding a simple line of best fit, to dimensional reduction, to deep neural networks. In this course, you will get a well-rounded introduction to Machine Learning and discover how and when it can help you in your research. Motivated by theoretical foundations throughout, you will learn to identify which algorithms are appropriate for use cases and learn best practices for preparing data and interpreting results. Topics covered include clustering methods, classical supervised techniques (support vector machines, random forests, and linear/logistic regression), and basic neural networks. Material will be reinforced with quizzes, coding assignments, and discussions, and a final project will allow students to explore a dataset of their choice using the methods they have learned. Coding will be in Python and will use popular libraries such as Numpy, Scikit-Learn, and Pytorch.

Learner Outcomes

When you complete the course successfully, you will be able to:

• Explain why and how Machine Learning algorithms are able to make inferences from data
• Choose an appropriate Machine Learning algorithm for a given problem
• Implement common Machine Learning methods in Python using popular libraries
• Follow best practices for acquiring and preparing data for Machine Learning
• Interpret Machine Learning output

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information

A textbook is required or recommended for this course.

Click here to view a textbook list for FAES courses and purchasing information. Please note that tuition does not include textbooks.