100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
logo-home
Previously searched by you
Previously searched by you
logo
Summary Bayesian Learning $7.49   Add to cart
Quickly navigate to
Preview
  2.  
  3. Bamberg University
  4.  
  5. Machine Leaning

Summary

Summary Bayesian Learning
 1 view  0 purchase
  • Course
  • Machine Leaning
  • Institution
  • Bamberg University

 Bayes Theorem  MAP, ML hypotheses  MAP learners  Minimum description length principle  Bayes optimal classi er  Naive Bayes learner  Example: Learning over text data  Bayesian belief networks  Expectation Maximization algorithm 125 lectur

Preview 4 out of 50  pages

Document information

  • January 2, 2022
  • 50
  • 2006/2007
  • Summary

Subjects

  •  bayes optimal classi er
  • naive bayes learner
  • bayes theorem
  •  map
  • ml hypotheses

Written for

  • Bamberg University
  • Machine Leaning
All documents for this subject (17)

Seller

avatar-seller
riyadhalgburi

Content preview

Bayesian Learning

[Read Ch. 6]
[Suggested exercises: 6.1, 6.2, 6.6]
 Bayes Theorem
 MAP, ML hypotheses
 MAP learners
 Minimum description length principle
 Bayes optimal classi er
 Naive Bayes learner
 Example: Learning over text data
 Bayesian belief networks
 Expectation Maximization algorithm




125 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997

, Two Roles for Bayesian Methods

Provides practical learning algorithms:
 Naive Bayes learning
 Bayesian belief network learning
 Combine prior knowledge (prior probabilities)
with observed data
 Requires prior probabilities
Provides useful conceptual framework
 Provides \gold standard" for evaluating other
learning algorithms
 Additional insight into Occam's razor




126 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997

, Bayes Theorem


P (D
P (hjD) = P (D) jh )P (h )

 P (h) = prior probability of hypothesis h
 P (D) = prior probability of training data D
 P (hjD) = probability of h given D
 P (Djh) = probability of D given h




127 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997

, Choosing Hypotheses


P (D
P (hjD) = P (D)jh )P (h )

Generally want the most probable hypothesis given
the training data
Maximum a posteriori hypothesis hMAP :
hMAP = arg max
h2H
P (hjD)
= arg max P (D jh )P (h )
h2H P (D)
= arg max
h2H
P (Djh)P (h)
If assume P (hi) = P (hj ) then can further simplify,
and choose the Maximum likelihood (ML)
hypothesis
hML = arg maxhi2H
P (Djhi)



128 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller riyadhalgburi. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for $7.49. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

75619 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 14 years now

Start selling
$7.49
  • (0)
  Add to cart