Training Resources – Penn AI Tech

A Basic Introduction to Scientific Research in a Lab

Ray — Sun, 05 Mar 2023 21:10:40 +0000

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.

AI Campus Program

Elizabeth — Thu, 11 Dec 2025 20:45:40 +0000

AI Campus is a collaborative, project-based-learning initiative designed to equip participants with the confidence and skills needed to apply artificial intelligence (AI) methods in their career or research. It brings together participants of diverse backgrounds with top AI experts from around the country to address challenging scientific problems using AI and machine learning (ML). This site provides information on the National AI Campus Program as well as the ‘medicine-focused’ AI Campus Program at the Cedars Sinai Medical Center. Included are training project resources focuses on students getting experience working with AI/ML tools on biomedical problems.

Artificial Intelligence Course (Health-AI)

Elizabeth — Thu, 19 Feb 2026 21:28:30 +0000

Educational lectures developed by Dr. Ryan Urbanowicz in 2026. This course will explore how AI can be a driving force for automated clinical decision support and medical discovery. We will explore concepts in logic, knowledge representation, expert systems for automated decision-making, search algorithms, uncertainty in reasoning, and other related topics that will enable you to develop, understand, and apply health AI solutions effectively and ethically. We will explore how AI encompasses and differs from machine learning and the distinction between inductive and deductive reasoning. In a practical sense, the course will provide you with the tools to organize, represent, interpret, and search biomedical data to derive knowledge, automate decisions, and make predictions while avoiding bias. This course was developed for the Cedars Sinai Health University graduate programs.

Decision and Classification Trees, Clearly Explained!!! (StatQuest)

Ray — Sun, 05 Mar 2023 21:17:53 +0000

Decision trees are part of the foundation for Machine Learning. Although they are quite simple, they are very flexible and pop up in a very wide variety of situations. This StatQuest covers all the basics and shows you how to … Read More

Foundations of Artificial Intelligence

Ray — Sun, 05 Mar 2023 21:07:40 +0000

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.

Gradient Boosting

Ray — Sun, 05 Mar 2023 21:24:27 +0000

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.

Introduction to Research in a Computational Lab

Ray — Sun, 05 Mar 2023 21:12:17 +0000

This video is intended to give students (high school, undergrad, and grad) as well as new staff, an overview what research is like in a ‘dry’ or computational laboratory.

Linear and Logistic Regression Models

Ray — Sun, 05 Mar 2023 21:21:23 +0000

The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)

Machine Learning Essentials for Biomedical Data Science

Ray — Sun, 05 Mar 2023 21:01:47 +0000

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.

Neural Networks/ Deep Learning (StatQuest)

Ray — Sun, 05 Mar 2023 21:14:59 +0000

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.