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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

NLP Practical: Part I - University of Cambridge · Naive Bayes Naive Bayes Classi er: c^ = argmax c2C P(cj~f) = argmax c2C P(c) Yn i=1 P(f ijc) Feature vector f~; most probable class

Fake News Detection - Machine Learningcs229.stanford.edu/proj2017/final-posters/5148514.pdf · Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python

Finding Structure in CyTOF Data - Machine Learningcs229.stanford.edu/proj2011/Achlioptas_finding_structure_in_cytof_data.pdfand T-Sne. 5. LOCALLY LINEAR EMBEDDING Locally Linear Embedding

argmaxFf( ii,;) argmax,,( - University of Illinois at Chicagozhangx/pubDoc/notes/mmmn_notes.pdf · For multi-class tasks ... most 'real-world' learning problems have more structure

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

Classification of Depositional Environments using Satellite Imagescs229.stanford.edu/proj2017/final-posters/5136405.pdf · 2018-01-04 · • Understanding sedimentary depositional

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

Playing DOOM using Deep Reinforcement Learningcs229.stanford.edu/proj2017/final-posters/5134269.pdfPlaying DOOM using Deep Reinforcement Learning {tdhoot, danikhan, bkonyi}@stanford.edu

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

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

On Di erentiating Parameterized Argmin and Argmax Problems ...sgould/papers/argmin-TR-2016.pdf · On Di erentiating Parameterized Argmin and Argmax Problems with Application to Bi-level

Evaluation Metrics - CS229: Machine Learningcs229.stanford.edu/section/evaluation_metrics.pdf · - Desired performance and current performance. - Measure progress over time (No Free

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

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

Detecting Pneumonia in Chest X-Rays with Supervised Learningcs229.stanford.edu/proj2017/final-reports/5231221.pdf · Detecting Pneumonia in Chest X-Rays with Supervised Learning Benjamin

What’s in that picture? VQA system - Machine Learningcs229.stanford.edu/proj2017/final-posters/5148340.pdfVisual Question Answering is a complex task which aims at answering a question

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

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

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

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

Collaborative Neighborhoods - Machine Learningcs229.stanford.edu/proj2014/Diego Represas, David Dindi,Collaborati… · Diego Represas, David Dindi diegorep,[email protected] December

Image&Classification - Machine Learningcs229.stanford.edu/proj2013/SchoendorfElder-ImageClassification.pdf · Discussion* Inourinitialdatafrom&the&milestone,wefoundthatwith&proper¶meters,all&

Lecture 22: Statistical Machine Translation€¦ · Statistical Machine Translation We want the best (most likely) [English] translation for the [Chinese] input: argmax English P(

Evaluation Metrics - Machine Learningcs229.stanford.edu/section/evaluation_metrics_fall17.pdf · 2017-10-30 · Evaluation Metrics (Classifiers) CS229 Section Anand Avati. Topics

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

FinalReport: SmartTrashNet: WasteLocalizationand …cs229.stanford.edu/proj2017/final-reports/5226723.pdfFinalReport: SmartTrashNet: WasteLocalizationand Classiﬁcation Oluwasanya

Deep RL For Starcraft II - Machine Learningcs229.stanford.edu/proj2017/final-reports/5234603.pdfDeep RL For Starcraft II Andrew G. Chang [email protected] Abstract Games have proven

Towards Automatic Icon Design using Machine Learningcs229.stanford.edu/proj2017/final-reports/5170191.pdf · 2018-01-04 · 99designs and Fiverr, or doing it themselves manually

Racing F-Zero with Imitation Learningcs229.stanford.edu/proj2017/final-reports/5243706.pdfRacing F-Zero with Imitation Learning Theory and Reinforcement Learning Stephanie Wang, Michael

Unsupervised Feature Learning for Reinforcement Learningcs229.stanford.edu/proj2011/Can-UnsupervisedFeatureLearningFor... · Unsupervised Feature Learning for Reinforcement Learning

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

Discovering Visual Hierarchy through Unsupervised Learningcs229.stanford.edu › proj2009 › Razvi.pdf · Discovering Visual Hierarchy through Unsupervised Learning Haider Razvi

Predicting Votes from Census Data - CS229: Machine Learningcs229.stanford.edu/proj2017/final-reports/5232542.pdf · After the 2016 presidential election, the U.S. census bureau sent

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,

Recognition of Tourist Attractions - Machine Learningcs229.stanford.edu/proj2016/report/LiLiYue-RecognitionOf...Recognition of Tourist Attractions Yuanfang Li ([email protected])