Machine Learning: Final Presentation James Dalphond James McCauley Andrew Wilkinson Phil Kovac Data Set: Yeast GOLD TEAM.

Slides:

Advertisements

Similar presentations

Here is a reminder of the arrangements for your exam period. ARRANGEMENTS FOR S4 EXAM PREPARATION LEAVE.

Advertisements

Adversarial Search: Game Playing Reading: Chapter next time.

|| Dmitry Laptev, Joachim M. Buhmann Machine Learning Lab, ETH Zurich 05/09/14Dmitry Laptev1 Convolutional Decision Trees.

Estimating Energy Efficiency of Buildings Matthew Wysocki.

Semantic Analysis of Movie Reviews for Rating Prediction

Speeding up multi-task learning Phong T Pham. Multi-task learning  Combine data from various data sources  Potentially exploit the inter-relation between.

Predicting Protein Interactions HERPES! Team Question Mark Jeff Brown Dante Kappotis Robert Vanderley Anthony Biasella.

Machine Learning Applying Our Knowledge Gold Team Week 3: Random Forests and SVM Results.

Herpes Jeff Brown Dante Kappotis Robert Vanderley Anthony Biasella.

Three kinds of learning

Bioinformatics Challenge  Learning in very high dimensions with very few samples  Acute leukemia dataset: 7129 # of gene vs. 72 samples  Colon cancer.

Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS Presentation

Team Question Mark Jeff Brown Dante Kappotis Robert Vanderley Anthony Biasella A little information on: HERPES presents.

Sparse vs. Ensemble Approaches to Supervised Learning

Predicting Matchability - CVPR 2014 Paper -

Machine Learning CS 165B Spring 2012

Department of Computer Science, University of Waikato, New Zealand Geoffrey Holmes, Bernhard Pfahringer and Richard Kirkby Traditional machine learning.

1 Machine Learning for Stock Selection Robert J. Yan Charles X. Ling University of Western Ontario, Canada {jyan,

Class Opener:. Example: Guided Notes: Word Problem Suppose you are selling cases of mixed nuts and roasted peanuts. You can order no more than a total.

© 2014 CY Lin, Columbia University E6893 Big Data Analytics – Lecture 4: Big Data Analytics Algorithms 1 E6893 Big Data Analytics: Financial Market Volatility.

TapPrints: Your Finger Taps Have Fingerprints Emiliano Miluzzo*, Alex Varshavsky*, Suhrid Balakrishnan*, Romit R. Choudhury + * at&t Labs – Research, USA.

Sorting. Pseudocode of Insertion Sort Insertion Sort To sort array A[0..n-1], sort A[0..n-2] recursively and then insert A[n-1] in its proper place among.

Combining multiple learners Usman Roshan. Bagging Randomly sample training data Determine classifier C i on sampled data Goto step 1 and repeat m times.

Application of Machine Learning for Sequential Data Peter Uherek.

Human pose recognition from depth image MS Research Cambridge.

Becoming a Fairtrade and a Forest School Staff Meeting to inform about Forest school concept (19/4/10) Level 3 training booked for three teachers: July.

COP5992 – DATA MINING TERM PROJECT RANDOM SUBSPACE METHOD + CO-TRAINING by SELIM KALAYCI.

WEEK 1 You have 10 seconds to name…

Random Forests Ujjwol Subedi. Introduction What is Random Tree? ◦ Is a tree constructed randomly from a set of possible trees having K random features.

Using Classification Trees to Decide News Popularity

WEEK4 RESEARCH Amari Lewis Aidean Sharghi. PREPARING THE DATASET  Cars – 83 samples  3 images for each sample when x=0  7 images for each sample when.

Competition II: Springleaf Sha Li (Team leader) Xiaoyan Chong, Minglu Ma, Yue Wang CAMCOS Fall 2015 San Jose State University.

… Algo 1 Algo 2 Algo 3 Algo N Meta-Learning Algo.

Combining multiple learners Usman Roshan. Decision tree From Alpaydin, 2010.

Machine Learning Usman Roshan Dept. of Computer Science NJIT.

Label Embedding Trees for Large Multi-class Tasks Samy Bengio Jason Weston David Grangier Presented by Zhengming Xing.

Combining Bagging and Random Subspaces to Create Better Ensembles

Erich Smith Coleman Platt

Trees, bagging, boosting, and stacking

Estimating Link Signatures with Machine Learning Algorithms

Brain State Modeling from EEG data through Machine Learning

Basic machine learning background with Python scikit-learn

Team 1: 32 responses Team 2: 55 responses Team 3: 29 responses

PM 598 RANK Education Your Life / pm598rank.com

© 2013 ExcelR Solutions. All Rights Reserved Examples of Random Forest.

© 2013 ExcelR Solutions. All Rights Reserved Data Mining - Supervised Decision Tree & Random Forest.

PM 598 RANK Education for Service-- pm598rank.com.

Feature Extraction Introduction Features Algorithms Methods

Machine Learning Week 1.

Combining Base Learners

Machine Learning Week 2.

ريكاوري (بازگشت به حالت اوليه)

Building Fractal Landscapes

مديريت موثر جلسات Running a Meeting that Works

Machine learning Empirical Performance Analysis

Overfitting and Underfitting

Ensemble learning.

Ensemble learning Reminder - Bagging of Trees Random Forest

Classification Breakdown

CS 322 week 3 Gold standard of science: testing (randomized programming and testing, one major component of software engineering). Sample stat reasoning.

Machine Learning / Deep Learning

Analysis for Predicting the Selling Price of Apartments Pratik Nikte

Project #6: Processing data from a visual IoT sensor of Fluid/Structure interactions with Machine Learning REU Students: Alexis Downing and Pete Orkweha.

Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learning REU Students: Pete Orkweha & Alexis Downing.

Multiple Organ detection in CT Volumes Using Random Forests - Week 5

Algorithms Lecture # 26 Dr. Sohail Aslam.

… 1 2 n A B V W C X 1 2 … n A … V … W … C … A X feature 1 feature 2

THE ASSISTIVE SYSTEM SHIFALI KUMAR BISHWO GURUNG JAMES CHOU

REU Report Meeting Week 9

Advisor: Dr.vahidipour Zahra salimian Shaghayegh jalali Dec 2017

Presentation transcript:

Machine Learning: Final Presentation James Dalphond James McCauley Andrew Wilkinson Phil Kovac Data Set: Yeast GOLD TEAM

WEEK 4 GOALS Combined our four datasets  Run the fast random forests algorithm. Run our data sets against other species  Arabidopsis.  Herpes. NOTE: For interspecies runs and same species, we applied the “Leave One Out” method as well as the full combined sets.

Best Performance All Sets 86.19%500 Trees and 29 Features Worst Performance No Local 85.21%1000 Trees and 117 Features Same Organism Testing

Yeast vs. Arabidopsis Best Performance No Primary65.04%500 Trees and 7 Features Worst Performance No Physiochemical56.61%500 Trees and 21 Features

All Arabidopsis Runs (Train on Yeast)‏

Yeast vs. Herpes Best Performance No Primary51.170%500 Trees and 27 Features Worst Performance No Local48.830%500 Trees and 26 Features

All Herpes Runs (Train on Yeast)‏

Herpes vs. Arabidopsis (Train on Yeast)