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A Basic Introduction to Scientific Research in a Lab

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This video is intended to give students (high school, undergrad, and grad) as well as new staff, an overview of some of the basic information regarding what scientific research involves including: the scientific method, conducting a literature search, the anatomy of primary source articles, scientific communication, the publishing process, broad goals of research labs, how to make a useful contributions to a research lab, and more.

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Internal | Artificial Intelligence | Training Resources | All Resources

Foundations of Artificial Intelligence

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Educational lectures for the course: “Foundations of Artificial Intelligence” developed by Dr. Ryan Urbanowicz in 2020 at the University of Pennsylvania’s Perelman School of Medicine. This is the first of three courses covering topics in artificial intelligence for application within the context of informatics and biomedical research. The course is divided into modules that cover (1) introductory/background materials, (2) logic, (3) other knowledge representation, (4) essentials of expert systems, (5) search, (6) uncertainty, and (7) advanced/auxiliary topics. These topics offer a global foundation for branches of AI application and research, including concepts that will later support a deeper understanding of inductive reasoning and machine learning. In a practical sense, this course focuses on how biomedical data can be organized, represented, interpreted, searched, and applied in order to derive knowledge, make decisions, and ultimately make predictions while avoiding bias. This course was assembled using content from a wide variety of textbooks, slides, and lectures by various authors and speakers on the relevant topics. Some lectures were prepared and given by guest lecturers and thus have not been posted. At the time of posting, this course is in its second year so any feedback is welcome regarding any mistakes or suggested improvements.

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Machine Learning | External | Training Resources | All Resources

Gradient Boosting

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Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it’s not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous value, like someone’s weight. We call this, “using Gradient Boost for Regression”. In the next video, we’ll work through the math to prove that Gradient Boost for Regression really is this simple. In part 3, we’ll walk though how Gradient Boost classifies samples into two different categories, and in part 4, we’ll go through the math again, this time focusing on classification.

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Machine Learning | Internal | Training Resources | All Resources

Machine Learning Essentials for Biomedical Data Science

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An educational playlist (including 11 videos) covering the key essentials for using machine learning as part of a data science analysis pipeline. While topics are primarily framed around applications in biomedicine, this content is broadly applicable to other domains. This series was prepared at the Cedars Sinai Medical Center in Los Angeles by Dr. Ryan Urbanowicz of the Department of Computational Biomedicine.

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Machine Learning | External | Training Resources | All Resources

Neural Networks/ Deep Learning (StatQuest)

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Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are “black boxes”, but that’s not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.

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