This document discusses how machines can make decisions using machine learning approaches. It provides an overview of machine learning vocabulary and techniques including supervised learning methods like regression and classification. It also discusses unsupervised learning and examples of clustering emails. The document then demonstrates simple linear and logistic regression models to predict outputs for given inputs. It discusses evaluating models through error measurement and mentions some other machine learning techniques. Finally, it provides an overview of neural networks including feedforward networks and different types like convolutional and recurrent neural networks.

1. How Machines can take decisions
Viju Chacko

2. About Me
Viju Chacko
https://www.linkedin.com/in/vijuchacko/
viju.Chacko@gmail.com
IoT Practice Head (Utilities ISU) @ Tata Consultancy Services

3. Agenda
• Introduction
• Machine Learning Vocabulary
• Machine Learning approaches
• Other ML Techniques
• Introduction to Neural Networks
• Different types of Neural Networks
• GPGPU : General-Purpose Computation on GPU
• Other AI Components: Within an Enterprise
• Q&A

4. A forgotten definition of Computer
“Computer is an electronic device that process information as per predefined
instruction/rules”

5. A forgotten definition of Computer
“Computer is an electronic device that process information as per predefined
instruction/rules”
and with Artificial Intelligence
“Computers can also look at past instances of an event, look at the
inputs and outputs, and then predict the outputs for given inputs
without pre-defined instructions/rules”

6. Why Artificial Intelligence
is so relevant now?
• Large scale Storage and
Faster Compute (but
smaller in size) are possible, all
available over Cloud*
* Digital Evolution Revolution

7. Machine Learning in our daily lives

8. An approach to Machine Learning - Human
Intelligence
• How do we make decisions while driving the car?
• How does an experienced stock trader places his order?
• How do a doctor diagnosis your illness?
Explicitly or Implicitly human beings learn to read (and understand
the impact) of the parameters

9. Machine Learning Vocabulary
Sepal Length Sepal Width Petal Length Petal Width Species
6.7 3.0 5.2 2.3 Virginica
6.4 2.8 5.6 2.1 Virginica
4.6 3.4 1.4 0.3 Setosa
6.9 3.1 4.9 1.5 Versicolor
4.4 2.9 1.4 0.2 Setosa
4.8 3.0 1.4 0.1 Setosa
5.9 3.0 5.1 1.8 Virginica
5.4 3.9 1.3 0.4 Setosa
4.9 3.0 1.4 0.2 Setosa
5.4 3.4 1.7 0.2 Setosa
Features
Target
Label

10. Types of Machine Learning
Machine
Learning
Supervised:
Supervised learning is
the machine learning
task of inferring a
function from labeled
training data.
Regression: Outcome
is continuous
(numerical)
Classification:
Outcome is a
category
Unsupervised:
Unsupervised learning is
a type of
machine learning
algorithm used to draw
inferences from datasets
consisting of input data
without labeled
responses.
Ex: Categorizing the emails to Primary, Social,
Promotions, Updates, Forums
Ex: Predicting Stock price
Ex: Explore the set a given data set and identify
possible classifications within the data

11. Training and Test Data from history
Training
Data
Test Data

12. A very Basic Linear Regression
History
Input
(x)
Output
(y)
1 6
4 18
3 14
2 10
6 26
2 10
3 14
5 22
6 26
2 10
1 6

13. A very Basic Linear Regression
History
Input
(x)
Output
(y)
1 6
4 18
3 14
2 10
6 26
2 10
3 14
5 22
6 26
2 10
1 6
Current Input
Input
(x)
Output
(y)
9 ?
8 ?
1 6
9  38
8 34

14. A very Basic Logistic Regression
Input
(x)
Output
(y)
90 4.54E-05
91 0.000123
92 0.000335
93 0.000911
94 0.002473
95 0.006693
96 0.017986
97 0.047426
98 0.119203
99 0.268941
100 0.5
101 0.731059
102 0.880797
103 0.952574
104 0.982014
105 0.993307
106 0.997527
107 0.999089
108 0.999665
109 0.999877
110 0.999955

15. A very Basic Logistic Regression
Input
(x)
Output
(y)
90 4.54E-05
91 0.000123
92 0.000335
93 0.000911
94 0.002473
95 0.006693
96 0.017986
97 0.047426
98 0.119203
99 0.268941
100 0.5
101 0.731059
102 0.880797
103 0.952574
104 0.982014
105 0.993307
106 0.997527
107 0.999089
108 0.999665
109 0.999877
110 0.999955
Current Input
Input
(x)
Output
(y)
75 ?
210 ?
75  Out (1)
210  In (0)

16. Fitting the Model

17. Error measurement – Efficiency of a model

18. Life is not always simple equations
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Stepwise Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
Applying mathematical/statistical approaches for finding the relationship
between inputs and outputs (Historical events)

19. Not just all .. there are other techniques as well
Support Vector Machine / Support Vector Classifier
Kernel Approximation : An approach of applying a simplification function over any input (A simple Definition)
Principal component analysis: An approach to take only the relevant features, or identify the relevant features
….

20. Ensemble Modeling- Just one model vs.
a bunch of them

21. It’s the system that
matters
If the bee disappeared off the surface of the globe,
then man would have only four years of life left. No
more bees, no more pollination, no more plants, no
more animals, no more man.
-- Albert Einstein
A closer look at another Machine
learning approach inspired from
this MACRO/MICRO universe

22. Neurons – Artificial / Natural
Activation
Function

23. A more closer look at an Artificial Neuron
x = inputs
w = weights
b = bias term
f(z) = output

24. Why Network of
Neurons?
• Real world problems cannot be mapped to
mathematical/statistical models
• Following the steps of nature

25. Feed forward neural network

26. Weights

27. Input Layer

28. Output Layer

29. Hidden layers

30. Training a Neural Network

31. Different types of Neural Networks
• Convolutional Neural Networks :A Convolutional neural network
(CNN, or ConvNet) is a class of deep, feed-forward artificial neural
networks that has successfully been applied to analyzing visual
imagery.
• Recurrent Neural Network(RNN): Hidden layers receive their own
outputs as input
• Long / short term memory (LSTM): An improvement over RNN
• Restricted Boltzmann machines (RBM),Deep Belief Networks (DBN) ,
Deep convolutional inverse graphics networks (DCIGN)….
*http://www.asimovinstitute.org/neural-network-zoo/
by Fjodor Van Veen

32. GPGPU : General-Purpose Computation on GPU
* Images are copyright material of subsequent brands

33. Other AI Components: Within an Enterprise

34. &
Questions?