1. 28 October 2017
Ritika Nevatia
Rakuten, Inc.

2. 28 October 2017
Ritika Nevatia
Rakuten, Inc.

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Purrr

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What is Machine Learning?
Components
Features
Feature Selection
Feature Scaling
Models
Training
Types
Supervised Learning
Linear Regression
Unsupervised Learning
Reinforcement Learning
Deep Learning

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Ritika Nevatia
Bachelor in Computer Engineering
University of Mumbai
Previously
Currently
@nevatiaritika

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Sound Facial Expression
Posture Time Since Last Meal Last Pampered
????
Shape of Whiskers

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Map the Problem -> Solution
Represent the characteristics
Number to each sound?
Rules to those numbers?
Next time new sound?
Other characteristics?

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Machine Learning

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Input
Output
Input
Program
Programming Machine Learning

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Sounds Made Facial Expression
Time since last
meal
Time since last
attention
Time since last
destructive act
Instance 1
(sound as
spectrogram)
(image as pixels) 10 sec 1 min 7 min
Instance 2
(sound as
spectrogram)
(image as pixels) 5 min 17 sec 1 hour
Instance 3
(sound as
spectrogram)
(image as pixels) 2 min 9 min 20 sec
Instance 4
(sound as
spectrogram)
(image as pixels) 90 min 1.2 min 19 min
Training Set

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Irrelevant Data
More features -> More work
Create Features (eg: ratio instead of two numbers)
Unyielding features

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Some features are large
1,000,000 to 10,000,000
Some features are small
0.1 to 1
Scale them both
100 to 1000

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Model artifact created after training an algorithm
Use trained models to predict new input’s outputs
Complex
Input set of features (n1, n2, n3…)
Output answer (“I was bored”)

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1. Raw Models
1. Blueprints
2. Wrong solutions?
2. Tweak nuts and bolts
1. Look at training set
2. Compare answer of model with the correct answer
3. Fix the values
4. Small Steps
5. Multiple trainings
3. Ready!

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Classification/
Categorization
Regression
Clustering
Dimensionality Reduction
Supervised Unsupervised
Discrete
Continuous

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Bored
Bored
Not
Bored
Supervised
Learning
Algorithm
Predictive
Model
Not
Bored

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Decision Tree
Based on inputs
That gives out one output
Eg: Decision trees, Logistic Regression,
Random Forest

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• Handwriting recognition (OCR - Optical Character Recognition)
• used at the post office to recognise addresses from envelopes for example.
• Spam detection
• keeps your inbox clean after seeing a great many examples of spam email.
• Object or behavior recognition from images or video
• detection and tracking of objects, behavior or beaches of interest.
• Speech recognition
• conversion of a recording of a voice to a representation in text form, used in commercial apps like Siri,
Apple's voice assistant.

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“Hello World”
Input -> x
Output -> y
Simple linear regression
Ordinary Least Squares
y = B0 + B1*x
Extend inputs -> x1, x2, x3…

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Unsupervised
Learning
Algorithm
Predictive
Model
Not
Bored

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How does it learn?
More data
Come up with new representation
- Patterns
- Groups
- Similarities
- Deviations
eg: Apriori algorithm, K-means

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• Discovering groups
• Identifying customer segments
• Anomaly Detection
• Internet scrutiny

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Semi supervised
Learning
Algorithm
Predictive
Model
Not
Bored
Bored
Not
Bored

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Method for training agents
Robots
Modify its actions based on reward signals
Internal Model - Policy
Select actions based on perceptions
Performed Well?
Reward
Performed Bad?
Punish
Update Policy
Select actions based on optimizing reward
Complex policy
Sub problems -> ML again?
Trial and Error

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• Robotics
• Teach tasks, walking
• Financial Market Trading
• Maximize profit
• Games – Atari

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Faster
Structure and functions of neuron
Extracts new features
Feature Learning from raw data
Wheels of car

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What is a neuron?

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Neural network
Multiple layers of neurons
Data passing
Output of one, input of another
Input Layers
Direct features
Hidden Layers
Cannot be manipulated

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Weight of each neuron
Influential
Topology of the network
Types of neurons
Connections with each other
http://www.asimovinstitute.org/neural-network-zoo/

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Python
Open Source
Google