Applied Machine Learning
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.
BIOF 309 Introduction to Python or equivalent coding experience; MATH 215 & 216 (Introduction to Linear Algebra with Applications in Statistics) or equivalent recommended.
When you complete the course successfully, you will be able to:
- Explain why and how Machine Learning algorithms are able to make extrapolations from data
- Identify the benefits and drawbacks of different Machine Learning algorithms and when to use them
- Implement common Machine Learning methods in Python using popular libraries
- Follow best practices for acquiring and preparing data and interpreting Machine Learning output