Course Description

Bundle Registration! Save a bundle and receive 30% off the individual course rate when you register for both BIOF 509 and BIOF 510!

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


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." - Mihir

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.


BIOF 309: Introduction to Python or equivalent coding experience;

MATH 215 & MATH 216: Introduction to Linear Algebra with Applications in Statistics or equivalent recommended.

If you are unsure that you meet the prerequisite requirements, please contact registrar@faes.org and provide information about your course of interest and background knowledge.

Follow the link to review FAES Tuition Refund Policy.

Funding Justification Guide

Some labs and institutes may have specific funds set aside for trainees to continue their education and professional development. FAES has created a guide intended to help trainees request funds that may be available and, if they are available, request use of the training funds for continued professional development. More details

To Register Click on "Add to Cart"
Section Title
Applied Machine Learning
Online Asynchronous
Aug 28, 2024 to Oct 15, 2024
Total Cost (Includes $75 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
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