This document provides an overview of machine learning. It begins with an introduction and discusses the basics, types (supervised, unsupervised, reinforcement learning), technologies, applications, and vision for the next few years. Key points covered include definitions of machine learning, examples of applications (search engines, spam filters, personalized recommendations), and descriptions of different problem types (classification, regression, clustering) and learning approaches (decision trees, neural networks, Bayesian methods).

1. Machine Learning
A YEAR SPENT IN
ARTIFICIAL INTELLIGENCE
ENOUGH TO MAKE ONE BELIEVE IN GOD” – ALAN PERLIS

2. Agenda
 Introduction
 Basics
 Types of Machine Learning
 Machine Learning Technologies
 Application
 Vision in next few years

3. Quick Questionnaire
 How many people have heard about Machine Learning ?
 How many people know about Machine Learning ?
 How many people are using Machine Learning ?

4. What is Machine Learning ?
 Subfield of Artificial Intelligence.
 First Arthur Samuel gave the concept of Machine Learning, In 1959.
 "Field of study that gives computers the ability to learn without being explicitly programmed“.
 Computer program is said to be learn from Experience (E) with some class of tasks (T) and
performance measure (P) if its performance at tasks in T as measured by P improves with E.

5. What is Machine Learning ?
 Explores the study and construction of algorithms that can learn from and make
predictions on data.
 Algorithms operate by building a model from example inputs.
 Data driven predictions or decisions.
 Unlike strictly static program instructions as we do.

7. Artificial Intelligence
 Machine Learning is the branch of the Artificial Intelligence.
 Inserting the learning capabilities just like humans into machines.
 Even the fastest supercomputer is 32 times slower than Human Brain.
 Predictions says that in 2o6o , we are able to form the digital brain like humans.
 NLP (Natural Language Processing ) is also based on the Machine Learning , more the data the
machine has , more its prediction goes to perfect.
 Titanic Disaster could be saved through Machine Learning.

8. Use of Machine Learning
 Google Search, Google News ,Page Ranking decided by Machine Learning.
 Upload images , automatically detects the face of your friend.
 Spam filter which is used to filter our mails from tones of spam mails.
 Right product for the right customers.

9. More applications
 Speech and hand-writing recognition
 Autonomous robot control
 Data mining and bioinformatics: motifs, alignment, …
 Playing games
 Fault detection
 Clinical diagnosis
 Credit scoring, fraud detection
 Web mining: search engines
 Market basket analysis

10. Why Machine Learning
 Human expertise does not exist (navigating on Mars)-
 TARS in Interstellar.
 Humans are unable to explain their expertise (speech recognition).
 Solution changes in time (routing on a computer network).
 Solution needs to be adapted to particular cases (user biometrics).

11. Terminology / Basic Terms
 Features – The numbers of features and distinct traits that can be used to describe
each item in quantitative manner.
 Samples – Sample is an item to process. It can document, picture, sound, video or
any other file contains data.
 Feature Vector – n dimensional vector that represents some object.
 Training Set – Set of data to discover potentially predictive relationships.

12. Terminology with Example
Features
Color – Red
Type- Logo
Shape
Features
Color – Light Blue
Type – Logo
Shape
Here sample are –both apples, Feature Vector =[Color, Type, Shape] , Training Set- Taken all at time

13. Categories

14. Types of Problems and Tasks
 Depending on the nature of the learning "signal" or "feedback" available to a learning system.
Supervised Learning
Unsupervised Learning
Reinforcement Learning

15. Example of Supervised Learning

16. Supervised Learning
 Learning from labelled data, and different set of training examples.
 Input and output is fixed.
 the goal is to learn a general rule that maps inputs to outputs.
 Or find the correlation to between input and output to find the algo which is
general to all the training examples.
 Input data called Vector & Output value called Supervisory signal.
 Presence of Expert or Teacher.
 E.g.- Neural Networks , Decision Trees , Bayesian Classification.

17. To solve Supervised Learning problem
 Determine the type of training examples.
 Decide what kind of data is to be used as a training set.
 Gather a training set.
 Set of input object and corresponding output is gathered.
 Determine the input feature representation of the learned function.
 The input object is transformed into a feature vector, which contains a number of features
that are descriptive of the object.

18. To solve Supervised Learning problem
 Determine the structure of the learned function and corresponding learning algorithm.
 Find out the function or algorithm which maps all the training sets.
 Just like bridge how input is connected with output.
 Complete the design.
 Addition of some control parameters & adjusted by optimizing performance.
 Evaluate the accuracy of the learned function.
 Check it is working properly or not, if not redesign again.

19. Supervised Learning Flow Chart
Raw Data Algorithm
Sample Data Trained
Product
Verification Production

20. Application
 Bioinformatics
 Database marketing
 Handwriting recognition
 Spam detection
 Pattern Recognition
 Speech Recognition

21. Unsupervised Learning
 No labels are given to the learning algorithm.
 Find structure in its input with the help of Clustering.
 Discover hidden patterns in data and find the suitable algorithm.
 As input is unlabeled, there is no error or reward signal to evaluate a potential solution. This makes
it different form others.
 Self guided learning algorithm.
 Plays important role in data mining methods to preprocess the data.
 Approaches to Unsupervised Learning – K means, hierarchical clustering, mixture models.

22. Unsupervised Learning

23. K- means / Hierarchical
 K means is a method of vector quantization.
 Partition of n observation into k cluster, and it belongs to nearest mean
 Popular of clustering analysis in data mining.
 NP Hard Problem.
 Hierarchical clustering builds a hierarchy of clusters.
 Agglomerative (Bottom Up Approach)
 Divisive (Top down Approach)

24. Applications

25. Difference Supervised Vs Unsupervised

26. Reinforcement Learning
 Program interacts with a dynamic environment.
 No explicit instructions.
 Decide its own whether it is near to goal or not.
 “Approximate Dynamic Programming”
 Unlike supervised learning correct input/output pairs are never presented.
 No optimization step is there like supervised learning to tell we have reached up to our goal.
 There is a focus on on-line performance.
 Finds a balance between exploration (of uncharted territory) and exploitation (of current
knowledge)

27. Basic Reinforcement Learning Model
 Set of environment states S.
 Set of actions A.
 Rules of transitions between states.
 Rules that determine the scalar immediate reward of transition.
 Rules that describe what the agent observes.

28. Algorithms used for Reinforcement Learning
 Criterion of optimality
 the problem studied is episodic, an episode ending when some terminal state is reached.
 Brute force (2 Step Policies)
 For each possible policy, sample returns while following it.
 Choose the policy with the largest expected return.
 1.Value function estimation 2. Direct policy search
 Value function approaches
 It finds the policy which return maximize but maintaining sets.
 Based on MKP(Markov Decision Parameters)

29. Applications of Reinforcement Learning
 Game theory
 Control theory
 Operations research
 Information theory
 Simulation-based optimization
 Multi-agent systems
 Swarm intelligence
 Statistics
 Genetic algorithms

30. Semi-Supervised Learning
 Semi-supervised learning is a class of supervised learning tasks.
 But it uses large amount of unlabelled data with the labelled data.
 Actually it falls between supervised learning and supervised learning.
 Assumptions used in semi-supervised learning.
 Smoothness assumption - Points which are close to each other are more likely to share a label.
 Cluster assumption - The data tend to form discrete clusters, and points in the same cluster are more
likely to share a label
 Manifold assumption - The data lie approximately on a manifold of much lower dimension than the
input space.

31. How ML used in Hospitals

32. Machine Learning Methods
based on
output of a machine-learned system

33. Another Categorization
 Based on “desired output” of a machine-learned system
Classification
Regression
Clustering

34. Classification
 Predict class from observations.
 Inputs are divided into two or more classes.
 Model assigns unseen inputs to one (or multi-label classification) or more of these classes.
 Spam filtering is an example of classification, where the inputs are email (or other) messages and
the classes are "spam" and "not spam"

35. Regression
 Relation between mean value of one variable and corresponding value of another
variable.
 Statistical method to find the relation between different variables.
 Predict the output with the training data and observations.
 Popular method – Logistic Regression or binary regression.
 The outputs are continuous rather than discrete.

36. Clustering
 Grouping a set of objects in such way that objects in the same group are similar to each other.
 Objects are not predefined.
 Grouping in meaningful group.
 Unlike in classification, the groups are not known beforehand, making this typically an
unsupervised task.
 Example – Man’s shoes , woman’s shoes , man’s t-shirt, woman’s t-shirts.
 So they are two category “man & woman” and “t-shirts & shoes”.

37. Popular Framework / Tools
 Weka
 Carrot2
 Gate
 OpenNLP
 LingPipe
 Mallet – Topic Modelling
 Gensim – Topic Modelling (Python)
 Apache Mahout
 Mlib – Apache Spark
 Scikit learn – Python

38. Difference
Classification
 Classification means to group the output
into class.
 Classification to predict the type of tumor
i.e. harmful or not using the training data
sets.
 If it is discrete / categorical variable , then
it is classification problem.
Regression
 Regression means to predict the output
value using training data.
 Regression to predict the price of the
house from training data sets.
 If it is real / continuous then it is
regressions problem.

39. Approaches

40. Decision Tree Learning
 Predictive model.
 Maps observations about an item to conclusions about the item's target value.
 Used in Statistics and data mining.
 Tree models where the target variable can take a finite set of values are called classification trees.
 Leaves represent class labels & branches represent conjunctions.
 When target variable can take continuous values - regression trees.
 In data mining, a decision tree describes data but not decisions.
 Example – Wikipedia

41. Artificial Neural Networks
 Inspired by Biological Neural Networks(Central Nervous System of animal).
 Used when there are large number of inputs and generally unknown.
 ANNs are generally presented as systems of interconnected "neurons" which exchange messages
between each other.
 Used to solve computer vision, speech recognition and handwriting recognition.
 Eg. In handwriting recognition
 1. Input neuron activated by the pixels of an input image.
 2. Weighted and transformed by a function, the activations of these neurons are then passed on to
other neurons.
 3. This process is repeated until finally, an output neuron is activated. This determines which character
was read.

42. Artificial Neural Networks Structure

43. Any Queries ?
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