Table Of Content

Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI Lecture 1 Yasemin Altun January 3, 2007 ADMINISTRATIVE STUFF I Lectures: Wednesday 3:30-6:00, TTI Room 201 I Office hours: Wednesday 10am-Noon I No text book. Reference reading will be handed out. I No homework, no exam I Presentation/Discussions: 40% of grade I Final Project: 60% of grade I Applyoneofmethodsdiscussedtoyourresearcharea(eg. NLP,vision,CompBio) I AddnewfeaturestoSVM-structorotheravailablepackages I Theoreticalwork I URL http://ttic.uchicago.edu/ altun/Teaching/CS359/index.html I Class mail list Prerequisites I Familiarity with I Probability: Randomvariables,densities,expectations, joint,marginal,conditionalprobabilities,Bayesrule, independence I Linearalgebra I Optimization: Lagrangianmethods Traditional Prediction Problems I Supervised Learning: Given input-output pairs, find a function that predict outputs of new inputs I Binaryclassification,labelclass{0,1} I Multiclassclassification,labelclass{0,...,m} I Regression,labelclass< I Unsupervised learning: Given only inputs, discover some structure, eg. clusters, outliers I Semi-supervised Learning: Given a few input-output pairs and many inputs, find a function to predict outputs of new inputs I Transduction: Given a few input-output pairs and many inputs, find a function to predict well on unlabeled inputs Key Components I 4 aspects of learning I Representation I Parameterizationandthehypothesisspace I Learningobjective I Optimizationmethod I Different settings lead to different learning methods I For prediction tasks, state-of-the art methods Support Vector Machines, Boosting, Gaussian Processes Discriminative Learning I All these methods are from the discriminative learning paradigm I (Treat inputs, outputs as random variables. X for input with instantiation x, Y for output with instantiation y. p(x) for probability (X = x) ) I Given an input x, they discriminate the target label y. eg. p(y|x) I Since conditioning on x, they can treat arbitrarily complex objects as input I Versus a generative approach, I wherethegoalistoestimatethejointdistributionp(x,y). I p(x,y)=p(y)p(x|y) I p(x|y): Giventhetargetlabel,generatetheinput. I eg. NaiveBayesclassifier Structured (Output) Prediction I Traditionally, discriminative methods predict one simple variable. I In real-life, it is rarely the case. I Not taking dependencies into account is an important shortcoming. I Domains: Natural Language Processing, Speech, Information Retrieval, Computer Vision, Bioinformatics, Computational Economy Examples I Domain: Natural Language Processing I Application: Part-of-speech tagging I Input: A sequence of words I Output: Labels of each word as noun,verb,adjective,etc. John hit the ball. Noun Vb Det Noun Examples I Domain: Computational Biology I Application: Protein Secondary Structure Prediction I Input: Amino-acid sequence AAY KSHGSGDYGDHDVGHPTPGDPWVEPDYGINVYH I Output: H/E/- regions HHHH——-EEEEEEEE—- – - - - HHHHH- - - - Examples I Domain: Computer vision I Application: Identifying joint angles of human body

Description:
All these methods are from the discriminative learning p(x|y): Given the target label, generate the input. Output: A (leaf) class from the taxomony
ebook img

Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI PDF

30 Pages·2007·0.6 MB·English
by  Yasemin Altun
Save to my drive
Quick download
Download

Download Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI PDF Free - Full Version

by Yasemin Altun| 2007| 30 pages| 0.6| English

Download Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI by Yasemin Altun in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!

Free Download PDF

About Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI

All these methods are from the discriminative learning p(x|y): Given the target label, generate the input. Output: A (leaf) class from the taxomony

Detailed Information

Author:Yasemin Altun
Publication Year:2007
Pages:30
Language:English
File Size:0.6
Format:PDF
Price:FREE
Download Free PDF

Safe & Secure Download - No registration required

Why Choose PDFdrive for Your Free Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI Download?

  • 100% Free: No hidden fees or subscriptions required for one book every day.
  • No Registration: Immediate access is available without creating accounts for one book every day.
  • Safe and Secure: Clean downloads without malware or viruses
  • Multiple Formats: PDF, MOBI, Mpub,... optimized for all devices
  • Educational Resource: Supporting knowledge sharing and learning

Free Books Similar to Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI

Frequently Asked Questions

Is it really free to download Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI PDF?

Yes, on https://PDFdrive.to you can download Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI by Yasemin Altun completely free. We don't require any payment, subscription, or registration to access this PDF file. For 3 books every day.

How can I read Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI on my mobile device?

After downloading Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI PDF, you can open it with any PDF reader app on your phone or tablet. We recommend using Adobe Acrobat Reader, Apple Books, or Google Play Books for the best reading experience.

Is this the full version of Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI?

Yes, this is the complete PDF version of Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI by Yasemin Altun. You will be able to read the entire content as in the printed version without missing any pages.

Is it legal to download Generative and Discriminative Approaches to Graphical Models CMSC 35900 Topics in AI PDF for free?

https://PDFdrive.to provides links to free educational resources available online. We do not store any files on our servers. Please be aware of copyright laws in your country before downloading.

The materials shared are intended for research, educational, and personal use in accordance with fair use principles.

We use cookies

We use cookies to ensure you get the best browsing experience on our website. By clicking "Accept Cookies", you agree that we can store cookies on your device in accordance with our Terms and Privacy.