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

Quarter: Spring
Day(s): Mon/Tues/Thur/Fri
Course Format: On-campus
Duration: 9 class sessions
Date(s): Mar 25—May 3
Time: 6:30—8:35 pm
Drop Deadline: Mar 27
Units: 2
Grade Restriction: No letter grade
Tuition: $485
Instructor(s): Gregory Ryslik
Status: Closed
Please Note: This course has a different schedule than what appears in the print catalogue. Please see the full course schedule below.
Spring
On-campus
Mon/Tues/Thur/Fri
6:30—8:35 pm
Date(s)
Mar 25—May 3
9 class sessions
Drop By
Mar 27
2 Units
Fees
$485
Grade Restriction
No letter grade
Instructor(s):
Gregory Ryslik
Closed
Please Note: This course has a different schedule than what appears in the print catalogue. Please see the full course schedule below.
Class schedule:

Session 1: Monday, March 25, 6:30 - 8:35 pm
Session 2: Tuesday, March 26, 6:30 - 8:35 pm
Session 3: Thursday, March 28, 6:30 - 8:35 pm
Session 4: Tuesday, April 2, 6:30 - 8:35 pm
Session 5: Thursday, April 4, 6:30 - 8:35 pm
Session 6: Thursday, April 25, 6:30 - 8:35 pm
Session 7: Friday, April 26, 6:30 - 8:35 pm
Session 8: Thursday, May 2, 6:30 - 8:35 pm
Session 9: Friday, May 3, 6:30 - 8:35 pm

Course description:

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 naturallanguage 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.

Please note: 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 nine 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, Vice President, Data Science, Mindstrong Health; Adjunct Assistant Professor of Statistics, Pennsylvania State

Gregory Ryslik is a statistician who has worked in the biotech, actuarial science, and automotive industries, including the data science team for service at Tesla and 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.
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)