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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.

Prerequisites

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

Learning Objectives:

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

Credentials:

This course applies toward the Bioinformatics Curiosity digital badge.

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