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

Quarter: Winter
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jan 11—Mar 19
Drop Deadline: Jan 14
Units: 2
Grade Restriction: No letter grade
Tuition: $625
Instructor(s): Gregory Ryslik
Limit: 100
Status: Registration opens Nov 30, 8:30 am (PT)
Please Note: Some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
Winter
Flex Online(About Formats)
Date(s)
Jan 11—Mar 19
10 weeks
Drop By
Jan 14
2 Units
Fees
$625
Grade Restriction
No letter grade
Instructor(s):
Gregory Ryslik
Limit
100
Registration opens Nov 30, 8:30 am (PT)
Please Note: Some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
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 and natural language processing. All of these advancements, however, are deeply rooted in the fields 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 AI and ML possible. Indeed, many of the newest algorithms, such as neural networks, random forests, and k-nearest neighbors, 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 will have a high-level understanding of common statistical tools used in AI and ML algorithms and be able to derive their own conclusions from statistical studies.

This course has no specific prerequisites and can be taken on a variety of levels. Beginners are encouraged to listen to the lectures and learn basic concepts. By the end of the course, beginners should have a sense of what these algorithms do. On a higher level, intermediate students can work some of the introductory problems that will be provided. On an advanced level (for students with a substantial math background who are interested in becoming data scientists), difficult math and stats problems will be covered. Students should be aware that this course will not make them an AI expert (this is not possible in ten sessions). On a basic level, the course will give students a taste of what statistics for AI is all about. On the highest level, the course will give students of a strong sense of what they need (and should be excited about) in order to pursue a career in this area.

Gregory Ryslik, Senior Vice President of Data Science, Machine Learning, and Digital Health Research, Compass Pathways; Adjunct Assistant Professor of Statistics, Pennsylvania State

Gregory Ryslik has held senior positions in the biotech, actuarial science, and automotive industries at companies including Tesla, Genentech, PricewaterhouseCoopers, and several startups. He received a PhD in biostatistics from Yale and an MA in statistics from Columbia.