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Course Description

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

Bundle Courses:

BIOF 509 | Applied Machine Learning

BIOF 510 | Advanced Applications of Artificial Intelligence

BIOF 509: 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 510: Enabled by large datasets, new algorithms, and hardware improvements, Artificial Intelligence is taking on increasingly complex tasks, in some cases transforming how scientific research is done. Building on the Machine Learning foundations from BIOF 509, BIOF 510 will explore how these recent advancements have enabled predictive algorithms to perform tasks that once required human intelligence. We will examine how different architectures and training regimens can tackle different types of data and problems, from basic multi-layer perceptrons to convolutional and recurrent neural networks, autoencoders, and Generative Adversarial Networks. In addition to looking at successful applications in the biomedical domain, we will also discuss the hazards of deploying Artificial Intelligence in science and best practices for ensuring trustworthy results. Concepts will be reinforced by discussions, quizzes, coding assignments, and a research project, in which students can apply the methods from the course to a dataset of their choice. Coding will be in Python and will use popular Deep Learning frameworks.

Learner Outcomes

When you complete these courses successfully, you will be able to:

  • Explain how neural networks are able to identify patterns in data
  • Choose an appropriate Deep Learning architecture for a given problem
  • Implement common Deep Learning architectures in Python using popular libraries
  • Follow best practices for acquiring and preparing data for Deep Learning
  • Interpret the output of Deep Learning systems

Microcredential(s)

These courses apply toward the Bioinformatics Endeavor digital badge.

What FAES Learners are Saying

"Dr. Ondov explains in a very straightforward manner, which makes it easy and simple to follow through on the lectures. Labs are fun and practical, with real world problems." -  FAES Learner

Textbook Information

A textbook is available for these courses.

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.

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.

Refund

Follow the link to review FAES Tuition Refund Policy.

If you cancel a course, the bundled price no longer applies and you need to pay the individual course and technology fees. 

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

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To Register Click on "Add to Cart"
Section Title
2-COURSE BUNDLE - Applied Machine Learning & Advanced Applications of Artificial Intelligence
Type
Online Asynchronous
Dates
Jan 29, 2025 to May 13, 2025
Total Cost (Includes $75 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (4 Credits) $1,130.00
Potential Discount(s)
Available for Academic Credit
4 Credit(s)
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