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STAT 05 — Statistics for Artificial Intelligence, Machine Learning, and Data Science: An Introduction

Quarter: Summer
Day(s): Tuesdays
Course Format: On-campus
Duration: 9 weeks
Date(s): Jun 26—Aug 28
Time: 7:00—9:05 pm
Drop Deadline: Jul 9
Units: 2
Tuition: $465
Instructor(s): Gregory Ryslik
Status: Registration opens on 05/29/2018
Please Note: No class on July 31
7:00—9:05 pm
Jun 26—Aug 28
9 weeks
Drop By
Jul 9
2 Units
Gregory Ryslik
Registration opens on 05/29/2018
Please Note: No class on July 31
There have been tremendous advancements in artificial intelligence (AI) and machine learning (ML) in recent years across a variety of fields ranging from autonomous driving, to disease prediction, to natural-language processing. All of these advancements, however, are deeply rooted in the field of statistics and computer science. This course will give students a high-level overview of some of the most common concepts in statistics that make ML and AI possible. Indeed, many of the newest algorithms, such as neural nets, random forests, k-nearest neighbors, and others, use statistics not only to build a model but also to evaluate its accuracy. The course will cover two broad areas of statistics: inference and prediction. The inference portion will introduce common statistical concepts that allow us to understand a population and test hypotheses (such as performing A/B tests and calculating and interpreting p-values). The prediction section will begin with the simplest of algorithms (linear regression) and gradually touch upon more advanced topics such as random forests and cross validation. Real-world examples will be used from the fields of healthcare, genetics, marketing, and manufacturing. By the end of the course, students should have a high-level understanding of common statistical tools used in AI and ML algorithms and be able to derive their own meaningful conclusions from statistical studies.

Gregory Ryslik, Vice President, Data Science, Mindstrong; Adjunct Assistant Professor of Statistics, Pennsylvania State

Gregory Ryslik is a statistician who has worked in the biotech, actuarial science, and automotive industries. Previously, he led the data science team for service at Tesla Motors, and performed nonclinical machine learning at Genentech. He received an MA in statistics from Columbia and a PhD in biostatistics from Yale.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.