Machine learning brain oftechnology

Machine LearningBrain of Technology

- Gopinath C

05/03/20232

© KPIT Technologies Limited

Contents – Machine Learning – Rise of the Brain Market overview / analysis Technology process Applications ML Landscape ML Overview Why, What and How of ML Need for ML/ Value additions Types of ML CNN Advancements of ML

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Demand Market Forecast

Similar numbers (CAGR-130%) forecast 2025.

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The scope enablers

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The technology process

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Traditional Programming

Machine Learning

ComputerData

ProgramOutput

ComputerData

OutputProgram

Machine Learning - programming

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Applications Web search Finance : Credit scoring,

fraud detection E-commerce Robotics Information extraction Social networks Debugging Space exploration Computational biology Retail: Market basket

analysis, Customer relationship management (CRM)

Manufacturing: Optimization, troubleshooting

Medicine: Medical diagnosis

Telecommunications: Quality of service optimization

Bioinformatics: Motifs, alignment

Example formation (feature and label

extraction)

Modeling

Evaluation

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Landscape

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ML Science and Data utilization

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ML Overview “A breakthrough in machine learning

would be worth ten Microsoft” (Bill Gates, Chairman, Microsoft)

Automating automation

Getting computers to program themselves

Let the data do the work instead!

▫Learn from past experiences

▫Improve the performances of intelligent programs

Classifier

New data

Presence or absence

Database1’st data Absence2’nd data Presence… …

Training

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AI Approach Reasoning with

Knowledge▫Knowledge base▫Reasoning

Traditional Approaches▫Handcrafted

knowledge base▫Complex reasoning

process▫Disadvantages

Knowledge acquisition bottleneck

Best move -New matrixOpponent’s

playing his move

Matrix representing the current board

Searching and

evaluating

Example: Chess Program

Example in retail: Customer transactions to consumer behavior:

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Why “Learn”? Machine learning is programming

computers to optimize a performance criterion using example data or past experience.

There is no need to “learn” to calculate payroll

Learning is used when:▫ Human expertise does not exist

(navigating on Mars),▫ 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)

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Feature Engineering Representation of the Real World

Data▫Features: data’s attributes

which may be useful in prediction

Feature Transformation and Selection▫Select a subset of the

features▫Construct new features,

e.g. Discretization of real value features Combinations of existing features

Post Processing to Fit the Classifier▫Does not change the

nature`

Intelligent Programs Value Functions

– Input: features– Output: value

Classifiers– Input: features– Output: a single

decision Sequence Labeling

– Input: sequence of features

– Output: sequence of decisions

Key issue in typical classifier, the results depends on feature engineering

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What is Machine Learning? Machine Learning

▫ Study of algorithms that improve their performance at some task with experience

Optimize a performance criterion using example data or past experience.

Role of Statistics: Inference from a sample

Role of Computer science: Efficient algorithms to▫ Solve the optimization

problem▫ Representing and evaluating

the model for inference

Example: Credit scoring

Differentiating between low-risk and high-risk customers from their income and savings

Discriminant: IF income > θ1 AND savings > θ2 THEN low-risk

ELSE high-risk

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ML FlowPattern recognitionFace recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Character recognition: Different handwriting styles.Speech recognition: Temporal dependency.

Use of a dictionary or the syntax of the language. Sensor fusion: Combine multiple modalities; eg, visual (lip image) and acoustic for speech

Medical diagnosis: From symptoms to illnessesWeb Advertising: Predict if a user clicks on an ad on the Internet.

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Choosing the Training Experience

Sometimes straightforward Text classification, disease diagnosis

▫Sometimes not so straightforward Chess playing

Other Attributes▫How the training experience is

controlled by the learner?▫How the training experience

represents the situations in which the performance of the program is measured?

Evaluation methods Accuracy Precision and

recall Squared error Likelihood Posterior

probability Cost / Utility Margin Entropy K-L divergence Etc.

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Target Function Choosing the Target

Function▫ What type of knowledge

will be learned?▫ How it will be used by the

program? Reducing the Learning

Problem▫ From the problem of

improving performance P at task T with experience E

▫ To the problem of learning some particular target functions

Solving Real World Problems What Is the Input?

- Features representing the real world data

What Is the Output?- Predictions or decisions to be made

What Is the Intelligent Program?- Types of classifiers, value functions, etc.

How to Learn from experience?- Learning algorithms

Eg: Whom to target -> What they will but -> How to retain them -> How to support them

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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

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Supervised Learning: Uses

• Prediction of future cases: Use the rule to predict the output for future inputs

• Knowledge extraction: The rule is easy to understand

• Compression: The rule is simpler than the data it explains

• Outlier detection: Exceptions that are not covered by the rule, e.g., fraud

Example: decision trees tools that create rules

Unsupervised Learning

Learning “what normally happens”

No output Clustering: Grouping similar

instances Other applications:

Summarization, Association Analysis

Example applications– Customer segmentation in CRM– Image compression: Color

quantization– Bioinformatics: Learning motifs

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CNN

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Key points CNN1. Visual scenes are generally, hierarchically organizedEg: input image -> primitive feature -> object parts -> objectForest image -> oriented edges -> bark, leaves -> trees2. Hard to train

over fittinglocal optima

3. Image statistics development4. Low level features are local5. High level features are coarser

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Future Technologies Big data and Cloud computing Internet of Things Artificial Intelligence

Robotics Machine learning

Advancement in ML Stereoscopic 3D learning and 2D testing Video based learning – Deep intense learning for automobile applications

https://www.gazemetrix.com/docs/products/photo-monitor

http://blueapp.io/blog/the-internet-of-things-and-machine-learning/

https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1

https://www.linkedin.com/pulse/machine-learning-demand-forecasting-pricing-vishnu-mohan

http://homepages.inf.ed.ac.uk/amos/publications/Storkey2011MachineLearningMarkets.pdf

References

https://www.gazemetrix.com/docs/products/photo-monitor









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