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.

6.
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.

7.
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.

8.
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

9.
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).

10.
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.

11.
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

12.
Categories

13.
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

14.
Example of Supervised Learning

15.
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.

16.
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.

17.
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.

18.
Supervised Learning Flow Chart
Raw Data AlgorithmSample Data Trained
Product
Verification Production

19.
Application
 Bioinformatics
 Database marketing
 Handwriting recognition
 Spam detection
 Pattern Recognition
 Speech Recognition

20.
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.

21.
Unsupervised Learning

22.
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)

23.
Applications

24.
Difference Supervised Vs Unsupervised

25.
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)

26.
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.

27.
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)

28.
Applications of Reinforcement Learning
 Game theory
 Control theory
 Operations research
 Information theory
 Simulation-based optimization
 Multi-agent systems
 Swarm intelligence
 Statistics
 Genetic algorithms

29.
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.

30.
How ML used in Hospitals

31.
Machine Learning Methods
based on
output of a machine-learned system

32.
Another Categorization
 Based on “desired output” of a machine-learned system
Classification
Regression
Clustering

33.
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"

34.
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.

35.
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”.

36.
Popular Framework / Tools
 Weka
 Carrot2
 Gate
 OpenNLP
 LingPipe
 Mallet – Topic Modelling
 Gensim – Topic Modelling (Python)
 Apache Mahout
 Mlib – Apache Spark
 Scikit learn – Python

37.
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.

38.
Approaches

39.
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

40.
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.

41.
Artificial Neural Networks Structure

42.
Any Queries ?
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