Course Description

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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.

What FAES Learners are Saying

"The lecture/learning materials tackled a broad range of perspectives (i.e. practical coding, mathematical theory, characteristics of specific methods/algorithms), which helped me develop a more holistic understanding of the topic." - FAES Learner

"[The instructor] has selected a crucial topic (Applied Machine Learning) and taught it in a profound, yet accessible way. Through pre-recorded lectures and guided coding homework, students had the ability to pick up on skills and apply them in a relevant capacity. He allowed for students to explore concepts that were interesting to them, providing further motivation to learn this exciting material. By the end of the course, I was able to apply complex machine learning tools that at first seemed so nebulous and out of reach, but now they are comfortably in my toolbox for research applications. He also provided extensive supplemental material, allowing each of us to easily expand upon our required learning. The course was well-structured, with clear guidance and modules for each week. I especially appreciate some of the clearest up-front expectations communicated, in the form of rubrics or in assignment instructions. I really enjoyed this class!" - FAES Learner
 

Textbook Information

A textbook is available for this course. 

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