BIOF 510
Advanced Applications of Artificial Intelligence

In the past decade, big data has become increasingly prominent in many fields, including healthcare and biomedical research. These increasingly large datasets pose a unique challenge to researchers. In these cases, nuanced approaches to machine learning are often necessary to extract important information. BIOF 510, a continuation of BIOF 509, will cover advanced applications of popular machine learning algorithms, including support vector machines, random forests, and neural networks. Neural network algorithms that will be covered include multi-layer perceptrons, convolutional neural networks, recurrent neural networks, probabilistic neural networks, and autoencoders. To reinforce key concepts, this course contains 4 written homework assignments and a research project. Through the homework assignments, students will (i) study theory behind common machine learning algorithms and (ii) explore examples of successful machine learning projects in biomedical research. For the research project, students will use python machine learning packages (Scikit-Learn, Tensorflow, Pytorch) to design a multistep pipeline to analyze a dataset of their choice. Students will also be expected to use Github to demonstrate proper documentation and version control practices when completing the project.

Prerequisites

The above course(s) or permission from the instructor.

Learning Objectives:

  • Choose appropriate machine learning techniques for data analyses and interpret their results
  • Design properly machine learning analysis pipelines and avoid common pitfalls
  • Complete a short research project using machine learning

Overview

Program

Class Type

Graduate Course

Credits

2

Availability

Spring 2021

Session

Session B

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