5147714 - Machine Learningcs229.stanford.edu/proj2017/final-posters/5147714.pdf · 2018-01-04 ·...

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Transcript of 5147714 - Machine Learningcs229.stanford.edu/proj2017/final-posters/5147714.pdf · 2018-01-04 ·...

Page 1: 5147714 - Machine Learningcs229.stanford.edu/proj2017/final-posters/5147714.pdf · 2018-01-04 · 202 Naïve Bayes =argmax PO') P(x DISCUSSION ... The data for this project comes

Optimizing Public Transit - Machine learningcs229.stanford.edu/proj2013/HuangLing-OptimizingPublicTransit.pdf · Optimizing Public Transit Mindy Huang Christopher Ling CS229 with

Optimizing Public Transit - Machine learningcs229.stanford.edu/proj2013/HuangLing-OptimizingPublicTransit.pdf · Optimizing Public Transit Mindy Huang Christopher Ling CS229 with

CS229 - Project Final Report: Automatic earthquake detection from ... - Machine Learningcs229.stanford.edu/proj2017/final-reports/5225419.pdf · 2018-01-04 · learning algorithms.

CS229 - Project Final Report: Automatic earthquake detection from ... - Machine Learningcs229.stanford.edu/proj2017/final-reports/5225419.pdf · 2018-01-04 · learning algorithms.

Hedging and Pricing Options { using Machine Learningcs229.stanford.edu/proj2009/KolindHarrisPrzybytkowski.pdf · Hedging and Pricing Options { using Machine Learning ... Introduction

Hedging and Pricing Options { using Machine Learningcs229.stanford.edu/proj2009/KolindHarrisPrzybytkowski.pdf · Hedging and Pricing Options { using Machine Learning ... Introduction

Music Genre Classification via Machine Learningcs229.stanford.edu/proj2017/final-reports/5244969.pdf · FMA dataset to classify 16 music genres given input features from music tracks,

Music Genre Classification via Machine Learningcs229.stanford.edu/proj2017/final-reports/5244969.pdf · FMA dataset to classify 16 music genres given input features from music tracks,

CS229 PROJECT REPORT - Machine learningcs229.stanford.edu/proj2017/final-reports/5237896.pdf · CS229 PROJECT REPORT Predicting Instagram tags with and without data Shreyash Pandey

CS229 PROJECT REPORT - Machine learningcs229.stanford.edu/proj2017/final-reports/5237896.pdf · CS229 PROJECT REPORT Predicting Instagram tags with and without data Shreyash Pandey

Object Detection using Convolutional Neural Networks - CS229: Machine Learningcs229.stanford.edu/proj2013/ReesmanMcCann-Vehicle... ·  · 2017-09-23Object Detection using Convolutional

Object Detection using Convolutional Neural Networks - CS229: Machine Learningcs229.stanford.edu/proj2013/ReesmanMcCann-Vehicle... ·  · 2017-09-23Object Detection using Convolutional

Naïve Bayes and Logistic Regression · Naïve Bayes and Logistic Regression Naïve Bayes •Generative Model "̂=argmax)*(")(-|") •Features assumed to be independent Logistic Regression

Naïve Bayes and Logistic Regression · Naïve Bayes and Logistic Regression Naïve Bayes •Generative Model "̂=argmax)*(")(-|") •Features assumed to be independent Logistic Regression

Reimaging Shallow Structure - Machine Learningcs229.stanford.edu › proj2016 › poster › DePaulWood-Re...Title: Reimaging Shallow Structure Author: Greg DePaul (gdepaul@stanford.edu)

Reimaging Shallow Structure - Machine Learningcs229.stanford.edu › proj2016 › poster › DePaulWood-Re...Title: Reimaging Shallow Structure Author: Greg DePaul ([email protected])

Predicting Chemical Reaction Type and ... - Machine Learningcs229.stanford.edu/proj2017/final-posters/5132644.pdf · [3] Nal Kalchbrenner et al. Neural machine translation in linear

Predicting Chemical Reaction Type and ... - Machine Learningcs229.stanford.edu/proj2017/final-posters/5132644.pdf · [3] Nal Kalchbrenner et al. Neural machine translation in linear

CS229 Final Report Automatic Melody Transcriptioncs229.stanford.edu › proj2017 › final-reports › 5244079.pdfAutomatic Music Transcription means automatically generating a musical

CS229 Final Report Automatic Melody Transcriptioncs229.stanford.edu › proj2017 › final-reports › 5244079.pdfAutomatic Music Transcription means automatically generating a musical

Outbrain: Click Prediction - Machine Learningcs229.stanford.edu/proj2016/poster/HoachuckSingh...Julien Hoachuck, Sudhanshu Singh, Lisa Yamada {juhoachu, ssingh9, lyamada}@stanford.edu

Outbrain: Click Prediction - Machine Learningcs229.stanford.edu/proj2016/poster/HoachuckSingh...Julien Hoachuck, Sudhanshu Singh, Lisa Yamada {juhoachu, ssingh9, lyamada}@stanford.edu

Bayesian Empirical likelihood - Stanford Universitystatweb.stanford.edu/~owen/pubtalks/bayesel-annotated.pdfPoint est. Interval / Test Parametric ^ = argmax R( ) 2log R( 0) !˜2 Non-parametric

Bayesian Empirical likelihood - Stanford Universitystatweb.stanford.edu/~owen/pubtalks/bayesel-annotated.pdfPoint est. Interval / Test Parametric ^ = argmax R( ) 2log R( 0) !˜2 Non-parametric

Learning Predictive Filters - Machine Learningcs229.stanford.edu/proj2013/McIntosh-LearningPredictiveFilters.pdf · 13/12/2013  · Learning Predictive Filters Lane McIntosh Neurosciences

Learning Predictive Filters - Machine Learningcs229.stanford.edu/proj2013/McIntosh-LearningPredictiveFilters.pdf · 13/12/2013  · Learning Predictive Filters Lane McIntosh Neurosciences

Results Ship Classification Using an Image Dataset ... - Machine Learningcs229.stanford.edu/proj2017/final-posters/5144117.pdf · machine learning, and computer vision techniques

Results Ship Classification Using an Image Dataset ... - Machine Learningcs229.stanford.edu/proj2017/final-posters/5144117.pdf · machine learning, and computer vision techniques

Outbrain Click Prediction - Machine Learningcs229.stanford.edu/proj2016/report/HoachuckSinghYamada... · 2017-09-23 · Outbrain Click Prediction Julien Hoachuck, Sudhanshu Singh,

Outbrain Click Prediction - Machine Learningcs229.stanford.edu/proj2016/report/HoachuckSinghYamada... · 2017-09-23 · Outbrain Click Prediction Julien Hoachuck, Sudhanshu Singh,

1 DENSE STEREO MATCHING USING MACHINE LEARNINGcs229.stanford.edu/...DenseStereoMatchingUsingMachineLearning.pdf · DENSE STEREO MATCHING USING MACHINE LEARNING ... sum of absolute

1 DENSE STEREO MATCHING USING MACHINE LEARNINGcs229.stanford.edu/...DenseStereoMatchingUsingMachineLearning.pdf · DENSE STEREO MATCHING USING MACHINE LEARNING ... sum of absolute

Racing F-ZERO with Imitation Learningcs229.stanford.edu/proj2017/final-posters/5140437.pdfRacing F-ZERO with Imitation Learning Overview I We are motivated by recent success of applying

Racing F-ZERO with Imitation Learningcs229.stanford.edu/proj2017/final-posters/5140437.pdfRacing F-ZERO with Imitation Learning Overview I We are motivated by recent success of applying

Music Transcription Using Deep Learningcs229.stanford.edu/proj2017/final-posters/5143634.pdf · Challenges: Music transcription, based on solid professional knowledge and experiences,

Music Transcription Using Deep Learningcs229.stanford.edu/proj2017/final-posters/5143634.pdf · Challenges: Music transcription, based on solid professional knowledge and experiences,

Amazon Recommendation Systems: Comparison Analysis …cs229.stanford.edu/proj2017/final-reports/5230053.pdfAmazon Recommendation Systems: Comparison Analysis between Traditional Techniques

Amazon Recommendation Systems: Comparison Analysis …cs229.stanford.edu/proj2017/final-reports/5230053.pdfAmazon Recommendation Systems: Comparison Analysis between Traditional Techniques

Learning Stochastic Inverses - Machine Learningcs229.stanford.edu/...LearningStochasticInverses.pdf · guesses the correct parse based on machine learning. The system sometimes returns

Learning Stochastic Inverses - Machine Learningcs229.stanford.edu/...LearningStochasticInverses.pdf · guesses the correct parse based on machine learning. The system sometimes returns