Machine learning is a branch of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal of machine learning is to build programs that can teach themselves to grow and change when exposed to new data. There are supervised, unsupervised, and reinforcement learning techniques used in machine learning applications across many fields including computer vision, speech recognition, robotics, healthcare, and finance.

1. Machine Learning an
Introduction
branch of AI or Artificial Intelligence,
since, the ability to change
experience into expertise or to detect
patterns in complex data is a mark of
human or animal intelligence.

2. Source Materials
• R. Duda, P. Hart & D. Stork, Pattern
Classification (2nd ed.), Wiley (Required)
• T. Mitchell, Machine Learning,
McGraw-Hill (Recommended)
• Papers

3. A Few Quotes
• “A breakthrough in machine learning would be worth
ten Microsofts” (Bill Gates, Chairman, Microsoft)
• “Machine learning is the next Internet”
(Tony Tether, Director, DARPA)
• Machine learning is the hot new thing”
(John Hennessy, President, Stanford)
• “Web rankings today are mostly a matter of machine
learning” (Prabhakar Raghavan, Dir. Research, Yahoo)
• “Machine learning is going to result in a real revolution”
(Greg Papadopoulos, CTO, Sun)
• “Machine learning is today’s discontinuity”
(Jerry Yang, CEO, Yahoo)

4. So What Is Machine Learning?
• Automating automation
• Getting computers to program themselves
• Writing software is the bottleneck
• Let the data do the work instead!

5. Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program

6. Machine Learning
• Programming the systems so as to make them automatically learn
and improve with experience.
• learning implies recognizing and understanding the input data and
taking informed decisions based on the supplied data.
• very difficult to consider all the decisions based on all possible
inputs.
• solve this problem, algorithms are developed that build knowledge
from a specific data and past experience
• Applying the principles of statistical science, probability, logic,
mathematical optimization, reinforcement learning, and control
theory

7. Applications
Vision processing
Language processing
Forecasting things like stock market
trends, weather
Pattern recognition
Games
Data mining
 Expert systems
 Robotics

8. Applications contd..
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• nformation extraction
• Social networks
• Debugging
• [Your favorite area]

9. Applications contd..
• Talking to the voice assistant on our
smart phones
• Recommending the right product for
our customers
• Stopping credit card fraud
• Filtering out spam from our e-mail
inboxes
• Detecting and diagnosing medical
diseases
• the list goes on and on.

10. Six components of Machine
Learning
• Data
• Tasks
• Models
• Loss Function
• Learning algorithm
• Evaluation

11. A machine learning project involves the following steps:
• Defining a Problem
• Preparing Data
• Evaluating Algorithms
• Improving Results
• Presenting Results

12. ML in a Nutshell
• Tens of thousands of machine learning
algorithms
• Hundreds new every year
• Every machine learning algorithm has
three components:
– Representation
– Evaluation
– Optimization

13. Representation
• Decision trees
• Sets of rules / Logic programs
• Instances
• Graphical models (Bayes/Markov nets)
• Neural networks
• Support vector machines
• Model ensembles
• Etc.

14. Evaluation
• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence
• Etc.

15. Optimization
• Combinatorial optimization
– E.g.: Greedy search
• Convex optimization
– E.g.: Gradient descent
• Constrained optimization
– E.g.: Linear programming

16. Types of Learning
• Supervised (inductive) learning
– Training data includes desired outputs
• Unsupervised learning
– Training data does not include desired outputs
• Semi-supervised learning
– Training data includes a few desired outputs
• Reinforcement learning
– Rewards from sequence of actions

17. Supervised Learning
• commonly used in real world applications,
such as face and speech recognition,
products or movie recommendations, and
sales forecasting.
• Regression and Classification.
• Regression trains on and predicts a
continuous-valued response, for example
predicting real estate prices.

18. Supervised learning
• learning data comes with description,
labels, targets or desired outputs and the
objective is to find a general rule that
maps inputs to outputs.
• learning data = labeled data.
• The learning rule is then used to label new
data with unknown outputs.
• Supervised learning involves building a
machine learning model that is based on
labeled samples

19. Supervised learning
• learning a function from available training
data.
• a learning algorithm analyzes the training
data
• produces a derived function
• used for mapping new examples.
• Logistic Regression, Neural networks,
Support Vector Machines (SVMs), and
Naive Bayes classifiers

20. Examples
• Classification attempts to find the
appropriate class label
• Analyzing positive/negative sentiment,
• male and female persons
• benign and malignant tumors
• secure and unsecure loans etc.

21. Unsupervised learning
• to detect anomalies, outliers, such as
fraud or defective equipment,
• to group customers with similar behaviors
for a sales campaign.
• opposite of supervised learning.
• no labeled data here.
• learning data contains only some
indications without any description or
labels

22. Semi-supervised Learning
• some learning samples are labelled, but
some other are not labelled.
• large amount of unlabeled data for training
and a small amount of labelled data for
testing
• expensive to acquire a fully labelled
dataset while more practical to label a
small subset

23. Semi-supervised Learning
• often requires skilled experts to label
certain remote sensing images,
• lots of field experiments to locate oil at a
particular location.
• acquiring unlabeled data is relatively easy.

24. Reinforcement Learning
• learning data gives feedback so that the
system adjusts to dynamic conditions in
order to achieve a certain objective.
• The system evaluates its performance
based on the feedback responses and
reacts accordingly.
• self-driving cars and chess master
algorithm AlphaGo

25. Inductive Learning
• Given examples of a function (X, F(X))
• Predict function F(X) for new examples X
– Discrete F(X): Classification
– Continuous F(X): Regression
– F(X) = Probability(X): Probability estimation

26. What We’ll Cover
• Supervised learning
– Decision tree induction
– Rule induction
– Instance-based learning
– Bayesian learning
– Neural networks
– Support vector machines
– Model ensembles
– Learning theory
• Unsupervised learning
– Clustering
– Dimensionality reduction

27. ML in Practice
• Understanding domain, prior knowledge,
and goals
• Data integration, selection, cleaning,
pre-processing, etc.
• Learning models
• Interpreting results
• Consolidating and deploying discovered
knowledge
• Loop

28. Libraries and Packages
• numpy - used for its N-dimensional array
objects
• pandas – data analysis library that
includes data frames
• matplotlib – 2D plotting library for
creating graphs and plots
• scikit-learn - algorithms used for data
analysis and data mining tasks
• seaborn –data visualization library based
on matplotlib

29. Purpose of Machine Learning
• purpose of machine learning is not
building an automated duplication of
intelligent behavior,
• but using the power of computers to
complement and supplement human
intelligence.
• For example, machine learning programs
can scan and process huge databases
detecting patterns that are beyond the
scope of human perception.