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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:

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.

Prerequisites

BIOF 309 (Introduction to Python) or equivalent coding experience; MATH 215 & MATH 216 (Introduction to Linear Algebra with Applications in Statistics) or equivalent recommended.

REFUND
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Section Title
Applied Machine Learning
Type
Online Asynchronous
Dates
Feb 01, 2023 to Mar 21, 2023
Total Cost (Includes $75 non-refundable technology fee per course)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
Instructor(s)
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