machine learning and applications
TRANSCRIPT
MACHINE LEARNING AND APPLICATIONS Geeta Arora
Expert Session delivered during Workshop on Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at University Institute of Engineering and Technology, Chandigarh
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
Machine Learning is not the future
Why Machine Learning?
Develop systems that can automatically adapt themselves to users
• Personalized news or emails
Discover new knowledge from large databases
Ability to mimic humans and replace monotonous tasks which require some intelligence
• Recognizing handwritten characters
Develop systems that are too difficult or expensive to
construct manually or knowledge tuned to a
specific task
Why the increased interest in Machine Learning? 1. Growing volumes and varieties of available data
2. Cheaper and more powerful computational processing
3. Better and inexpensive storage capacities
4. Open source revolution
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
What is Machine Learning?
Field of study that gives the computers the ability to learn without being explicitly programmed. (Arthur Sameul, 1959)
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
Problem –
Distinguish Apple from Orange
Write a function which takes and image file and outputs if it is an apple or not?
Start writing rules…
Rules start breaking ….
Lets solve with machine learning
Classifier
Apple
How do you solve the problem using Machine Learning?
Collect Training Data
Train Classifier Make
Predictions
Collect Training Data
Train Classifier Make
Predictions
Feature 1 Feature 2 Example
Example
The Code – Hello World of Machine Learning
from sklearn import tree features = [[140, "smooth"], [130, "smooth"], [150, "bumpy"], [170, "bumpy"]] labels = ["apples", "apples", "orange", "orange"] features = [[140, 1], [130, 1], [150, 0], [170, 0]] labels = [0, 0, 1, 1] clf = tree.DecisionTreeClassifier() clf.fit(features, labels) print(clf.predict([[160, 0]]))
clf.fit(features, labels) Under the hood…
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
Types of Machine Learning
Machine Learning
Supervised
Regression
Classification
Unsupervised Clustering
Reinforcement Recommendations
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
Machine Learning Workflow
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries s
Applications
Demo
Workflow
Machine Learning Libraries
What is Machine Learning?
Hello World of Machine Learning
Types of Machine Learning
Why Machine Learning?
AGENDA
Libraries
Applications
Demo
Workflow
Gmail: ensuring a spam-free inbox with Machine Learning
Google's Smart Email Reply
Google Photos uses machine learning to create customized albums
Google Keyboard
Google Speech Recognition
Apple Siri
Amazon Echo
https://play.google.com/store/apps/details?id=com.scigh.cricketworldcup2015
DEMO Scigh Cricket App
