Introduction(00:00:00 - 00:00:06) - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Introduction(00:00:00 - 00:00:06)
Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa S...
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2021/#schedule

0:00 Introduction
0:06 Bayesian networks: smoothing
0:11 Review: maximum likelihood
1:49 Laplace smoothing example
3:45 Laplace smoothing Key idea: maximum likelihood with Laplace smoothing
4:47 Interplay between smoothing and data
6:16 Summary

#bayesian

#Stanford #AI

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