The Presentation inside:

Overview of Machine Learning & Feature Engineering Machine Learning 101 Tutorial Strata + Hadoop World, NYC, Sep 2015 Alice Zheng, Dato 1

About us Chris DuBois Intro to recommenders Alice Zheng Overview of ML Piotr Teterwak Intro to image search & deep learning Krishna Sridhar Deploying ML as a predictive service Danny Bickson TA Alon Palombo TA

Why machine learning? Model data. Make predictions. Build intelligent applications.

Classification Predict amongst a discrete set of classes 4

5 Input Output

Spam filtering data prediction Spam vs. Not spam

Text classification EDUCATION FINANCE TECHNOLOGY

Regression Predict real/numeric values 8

Stock market Input Output

Similarity Find things like this 10

Similar products Product I’m buying Output: other products I might be interested in

Given image, find similar images http://www.tiltomo.com/

Recommender systems Learn what I want before I know it 13

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Playlist recommendations Recommendations formcoherent & diverse sequence

Friend recommendations Users and “items” are ofthe same type

Clustering Grouping similar items 17

Clustering images Goldberger et al. Set of Images

Clustering web search results

Machine learning … how? Data Answers I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Many systems Many tools Many teams Lots of methods/jargon

The machine learning pipeline I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Raw data Features Models

Three things to know about ML Feature = numeric representation of raw data Model = mathematical “summary” of features Making something that works = choose the right model and features, given data and task

Feature = numeric representation of raw data

Representing natural text It is a puppy and it is extremely cute. What’s important? Phrases? Specific words? Ordering? Subject, object, verb? Classify: puppy or not? Raw Text

Representing natural text It is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Sparse vector representation

Representing images Image source: “Recognizing and learning object categories,” Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009. Raw image: millions of RGB triplets, one for each pixel Raw Image

Representing images Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 -1.9236.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation

Feature space in machine learning Raw data ? high dimensional vectors Collection of data points ? point cloud in feature space Feature engineering = creating features of the appropriate granularity for the task

Crudely speaking, mathematicians fall into two categories: the algebraists, who find it easiest to reduce all problems to sets of numbers and variables, and the geometers, who understand the world through shapes.-- Masha Gessen, “Perfect Rigor”

Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry (Euclidean space)

Visualizing a sphere in 2D x2 + y2 = 1

Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1

Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1

Why are we looking at spheres? = = = = Poincare Conjecture: All physical objects without holes is “equivalent” to a sphere.

The power of higher dimensions A sphere in 4D can model the birth and death process of physical objects High dimensional features can model many things

Visualizing Feature Space

The challenge of high dimension geometry Feature space can have hundreds to millions of dimensions In high dimensions, our geometric imagination is limited Algebra comes to our aid

Visualizing bag-of-words I have a puppy and it is extremely cute

Visualizing bag-of-words puppy cute 1 1 1 extremely

Document point cloud word 1 word 2

Model = mathematical “summary” of features

What is a summary? Data ? point cloud in feature space Model = a geometric shape that best “fits” the point cloud

Clustering model Feature 2 Feature 1 Group data points tightly

Classification model Feature 2 Feature 1 Decide between two classes

Regression model Target Feature Fit the target values

Visualizing Feature Engineering

When does bag-of-words fail? puppy cat 2 1 1 have Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information 1

Improving on bag-of-words Idea: “normalize” word counts so that popular words are discounted Term frequency (tf) = Number of times a terms appears in a document Inverse document frequency of word (idf) = N = total number of documents Tf-idf count = tf x idf

From BOW to tf-idf puppy cat 2 1 1 have idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 1

From BOW to tf-idf puppy cat 1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 1 log 4 log 4 Tf-idf flattens uninformative dimensions in the BOW point cloud

Entry points of feature engineering Start from data and task What’s the best text representation for classification? Start from modeling method What kind of features does k-means assume? What does linear regression assume about the data?

Dato’s Machine Learning Platform

Dato’s machine learning platform Raw data Features GraphLab Create Dato Distributed Dato Predictive Services

Data structures for feature engineering Features SFrames SGraphs

Machine learning toolkits in GraphLab Create Classification/regression Clustering Recommenders Deep learning Similarity search Data matching Sentiment analysis Churn prediction Frequent pattern mining And on…

Demo

Dimensionality reduction Feature 1 Feature 2 Flatten non-useful features PCA: Find most non-flat linear subspace

PCA : Principal Component Analysis Center data at origin

PCA : Principal Component Analysis Find a line, such that the average distance of every data point to the line is minimized. This is the 1st Principal Component

PCA : Principal Component Analysis Find a 2nd line, - at right angles to the 1st - such that the average distance of every data point to the line is minimized. This is the 2nd Principal Component

PCA : Principal Component Analysis Find a 3rd line - at right angles to the previous lines - such that the average distance of every data point to the line is minimized. … There can only be as many principle components as the dimensionality of the data.

Demo

Coursera Machine Learning Specialization Learn machine learning in depth Build and deploy intelligent applications Year long certification program Joint project between University of Washington + Dato Details: https://www.coursera.org/specializations/machine-learning

Next up today [email protected] @RainyData, #StrataConf 11:30am - Intro to recommenders Chris DuBois 1:30pm - Intro to image search & deep learning Piotr Teterwak 3:30pm - Deploying ML as a predictive service Krishna Sridhar

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