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

Quarter: Fall
Day(s): Mondays
Course Format: Live Online (About Formats)
Duration: 10 weeks
Date(s): Sep 20—Nov 29
Time: 5:30—7:20 pm (PT)
Refund Deadline: Sep 22
Units: 2
Grade Restriction: No letter grade
Tuition: $530
Instructor(s): Gregory Ryslik
Class Recording Available: Yes
Status: Closed
Please Note: No class on November 22
DOWNLOAD THE SYLLABUS » (subject to change)
Fall
Live Online(About Formats)
Mondays
5:30—7:20 pm (PT)
Date(s)
Sep 20—Nov 29
10 weeks
Refund Date
Sep 22
2 Units
Fees
$530
Grade Restriction
No letter grade
Instructor(s):
Gregory Ryslik
Recording
Yes
Closed
Please Note: No class on November 22
DOWNLOAD THE SYLLABUS » (subject to change)
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 from the fields of healthcare, genetics, marketing, and manufacturing will be used. By the end of the course, students will have a high-level understanding of common statistical tools used in AI and ML algorithms and will be able to derive their own conclusions from statistical studies.

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.

Textbooks for this course:

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