about machine learning

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.