Introduction to Machine
• What is Machine Learning?
• Types of Machine Learning
• Decision Tree and Random Forests
• Neural Network
• Deep Learning
• Measuring Performance of ML algorithms
• Pitfalls of Machine Learning
What is Machine Learning?
• Definition by Arthur Samuel - Machine Learning is a technique which gives
"computers the ability to learn without being explicitly programmed.“
• To mimic human intelligence or human learning process
How Humans learn?
• Knowledge Transfer –attending
• Hit and Trial – learning cycling
during your childhood
What is actually
We asked Machines to do same
• Categorization • Prediction
Supervised Machine learning
• Formal boring definition - Supervised learning task of inferring a function from
labeled training data. The training data consist of a set of training examples. In
supervised learning, each example is a pair consisting of an input object (typically
a vector) and a desired output value (also called the supervisory signal).
• Layman term – Make computers learn from experience
• Task Driven
Example of supervised Machine
Categorizing whether tumor is
malignant or benign
Predicting the house of price in
How is Supervised Learning Achieved?
• The existing data is usually divided into
training or testing set. For e.g.,
Training is usually 70% of data where as
Testing is 30% of data.
• Algorithm develops it model based on
• Features important for model is usually
selected by humans
• Algorithm predicts the results for
testing data and later the predicted
value is compared with real value to
give us accuracy.
• Several algorithms are tried until
required accuracy is achieved
Unsupervised learning is a type of machine learning algorithm used to draw
inferences from datasets consisting of input data without labeled responses.
It is data driven.
Clustering a tumor of same kind
but doesn’t know it’s nature.
UnSupervised Learning Examples
Network Intrusion detection Clustering your customer base
• Reinforcement learning is a setting where we have a sequential decision
problem. Making a decision now influences what decisions we can make in the
future. A reward function is provided that tells us how “good” certain states are.
• For e.g., Making robot learn against worthy opponent to play table tennis
• No direct training examples – (delayed) rewards later
• Need for exploration of environment or exploitation of environment
• The environment might be stochastic and/or unknown
• The actions of the learner affects future rewards
Decision is a simple representation for
Decision tree learning is one of the
most successful techniques for
supervised classification learning.
For e.g., Surviving Titanic is famous first
Machine Learning explanation for
• Random Forest Tree is a Supervised Machine Learning Algorithm Based on Decision
• It is Collective Decisions of Different Decision Trees.
• In random forest, there is never a decision tree which have all features of all other
• Form a large set of simple features
• Initialize weights for training images
• For T rounds
• Normalize the weights
• For available features from the set, train a classifier using a single feature and evaluate
the training error
• Choose the classifier with the lowest error
• Update the weights of the training images: increase if classified wrongly by this
classifier, decrease if correctly
• Form the final strong classifier as the linear combination of the T classifiers
(coefficient larger if training error is small)
• a computer system modelled on the human brain and nervous system
Neural Network Example
Predicting whether the person goes to Hospital
In next 30 days based on historical Data ( Classification)
• It is usually done on time-series data
to predict the future trend
• For e.g., forecasting Stock value
based on historical data
• Usually achieved by ARIMA (Auto-
regressive integrated Moving
Amazon stock forecast
Amazon actual stock performance
Pitfalls of Machine Learning
• Over fitting
• Trying to make algorithm to work only for small set of data
• Ignoring Human intuition
• Forecasting usually fails because the algorithm is not able to gauge what humans consider
• Machine Bias
• For example, a 2015 study found that women were less likely to be shown high-income job ads by
Google's AdSense and another study found that Amazon’s same-day delivery service was
systematically not available in black neighborhoods, both for reasons that the companies could
not explain, but were just the result of the black box methods they used
Participate in Machine Learning