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Machine Learning: Artificial
Intelligence isn't just a Science
Fiction topic
Raul Garreta - Tryolabs / MonkeyLearn
My Credentials
● Computer Science Engineer from Udelar,
Msc in Machine Learning + NLP
● Co-Founder, CTO & Product Manager ...
Contents
● Brief intro to AI & Machine Learning (ML)
● ML Applications
● Cloud ML tools
What is AI?
From a behavioral point of view, is an artificial
agent that shows certain characteristics of
intelligence lik...
What is AI?
Behavioral test = Turing Test
If I write an enough complex If-
then-else structure, could it
pass the test?
Ra...
Different fields within AI
Artificial Intelligence
● General Artificial Intelligence
● Expert Systems
○ Natural Language P...
Machine Learning
Algorithms that allow computers
to automatically learn to perform
a task from data.
Can improve their per...
Machine Learning Definitions
Arthur Samuel (1959): "Field of study that gives computers
the ability to learn without being...
Machine Learning Algorithms
● Learn to associate a particular input (set of
features) to a particular output (class,
numbe...
Inputs: Instances
Usually we have instances of data that
represent objects: documents, images, users,
etc.
And can be repr...
Machine Learning Problems
Classification: assign a label (class)
to a set of items.
Regression: assign a number
(evaluatio...
Type of Machine Learning
Algorithms
Decision TreesLinear Models
Type of Machine Learning
Algorithms
Probabilistic /
Statistical Models
Neural Networks /
Deep Learning
Important Concepts in ML
Besides the Machine Learning…
● Data gathering / importation
● Data preprocessing
● Feature extra...
Applications
Natural Language Processing
Text Mining Speech to Text
Applications:
Computer Vision
Face Recognition OCR
Applications
Data Mining / Predictive Analytics
Recommendation Engines Medicine
Applications
Intelligent Agents
Robotics Game Players
Why use Machine Learning?
● Solve problems that manually would be extremely
difficult or impossible.
● Make predictions.
●...
● Avoid to deploy and maintain the full stack.
● Be cross platform.
● Not all programming languages have ML
tools.
● ML re...
As with other problems (eg: payments,
communications) is a trend to go SaaS.
Machine Learning Platforms
Machine Learning
Microsoft Azure ML
● http://azure.microsoft.com/en-
us/services/machine-learning/
● Launched preview version on June 2014....
Microsoft Azure ML
● Easy to scale, Azure infrastructure.
● Users can build custom R modules.
● GUI and APIs.
● More orien...
MonkeyLearn
● http://monkeylearn.com/
● Launched private alpha on April 2014
● Cloud based, focused on Text Mining:
extrac...
MonkeyLearn
● Easy to use.
● Pre-trained modules for different
applications.
● GUI and APIs.
● More oriented to developers...
Conclusions
● Machine Learning can allow
us to make intelligent apps.
● It's a trendy topic…
● New ML platforms are
emergi...
Machine Learning: Artificial Intelligence isn't just a Science Fiction topic
Machine Learning: Artificial Intelligence isn't just a Science Fiction topic
Machine Learning: Artificial Intelligence isn't just a Science Fiction topic
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Machine Learning: Artificial Intelligence isn't just a Science Fiction topic

In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.

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Machine Learning: Artificial Intelligence isn't just a Science Fiction topic

  1. 1. Machine Learning: Artificial Intelligence isn't just a Science Fiction topic Raul Garreta - Tryolabs / MonkeyLearn
  2. 2. My Credentials ● Computer Science Engineer from Udelar, Msc in Machine Learning + NLP ● Co-Founder, CTO & Product Manager at Tryolabs. ● Co-Founder at MonkeyLearn. ● Professor in ML at InCo, Udelar. ● Co-authored "Learning Scikit-learn: Machine Learning in Python"
  3. 3. Contents ● Brief intro to AI & Machine Learning (ML) ● ML Applications ● Cloud ML tools
  4. 4. What is AI? From a behavioral point of view, is an artificial agent that shows certain characteristics of intelligence like: ● Reasoning ● Knowledge representation ● Learning ● Planning ● Perception
  5. 5. What is AI? Behavioral test = Turing Test If I write an enough complex If- then-else structure, could it pass the test? Random behavior?
  6. 6. Different fields within AI Artificial Intelligence ● General Artificial Intelligence ● Expert Systems ○ Natural Language Processing ○ Computer Vision ○ Machine Learning ○ ...
  7. 7. Machine Learning Algorithms that allow computers to automatically learn to perform a task from data. Can improve their performance over time, by adding more data.
  8. 8. Machine Learning Definitions Arthur Samuel (1959): "Field of study that gives computers the ability to learn without being explicitly programmed" Tom Mitchell (1997): "A computer program is said to learn if its performance at a task T, as measured by a performance P, improves with experience E"
  9. 9. Machine Learning Algorithms ● Learn to associate a particular input (set of features) to a particular output (class, number or group of instances) ● That is the process of training a ML model. ● And use the learned model to predict the outcome on new instances
  10. 10. Inputs: Instances Usually we have instances of data that represent objects: documents, images, users, etc. And can be represented by a set of features: ● A document is represented by a set of words. ● An image is represented by a set of pixels. ● A user can be represented by the age, level of education, gender, interests, etc.
  11. 11. Machine Learning Problems Classification: assign a label (class) to a set of items. Regression: assign a number (evaluation) to a set of items Clustering: group items into clusters according to a similarity measure
  12. 12. Type of Machine Learning Algorithms Decision TreesLinear Models
  13. 13. Type of Machine Learning Algorithms Probabilistic / Statistical Models Neural Networks / Deep Learning
  14. 14. Important Concepts in ML Besides the Machine Learning… ● Data gathering / importation ● Data preprocessing ● Feature extraction ● Feature selection ● Performance evaluation (testing)
  15. 15. Applications Natural Language Processing Text Mining Speech to Text
  16. 16. Applications: Computer Vision Face Recognition OCR
  17. 17. Applications Data Mining / Predictive Analytics Recommendation Engines Medicine
  18. 18. Applications Intelligent Agents Robotics Game Players
  19. 19. Why use Machine Learning? ● Solve problems that manually would be extremely difficult or impossible. ● Make predictions. ● Automatically process huge amounts of information and sources: big data. ● Intelligent apps => improve UX => improve conversion rates => $$$ ● Great companies use it...
  20. 20. ● Avoid to deploy and maintain the full stack. ● Be cross platform. ● Not all programming languages have ML tools. ● ML requires huge amounts of computer power. ● Just solve it: good, fast, easy. Why use a Cloud Saas ML platform?
  21. 21. As with other problems (eg: payments, communications) is a trend to go SaaS. Machine Learning Platforms Machine Learning
  22. 22. Microsoft Azure ML ● http://azure.microsoft.com/en- us/services/machine-learning/ ● Launched preview version on June 2014. ● Cloud based ML platform to build predictive numerical applications. ● Technologies used in Xbox and Bing. Machine Learning
  23. 23. Microsoft Azure ML ● Easy to scale, Azure infrastructure. ● Users can build custom R modules. ● GUI and APIs. ● More oriented to Data Scientists. ● Pricing: pay as you go. Machine Learning
  24. 24. MonkeyLearn ● http://monkeylearn.com/ ● Launched private alpha on April 2014 ● Cloud based, focused on Text Mining: extract and classify information from text.
  25. 25. MonkeyLearn ● Easy to use. ● Pre-trained modules for different applications. ● GUI and APIs. ● More oriented to developers. ● Pricing: freemium, pay as you go.
  26. 26. Conclusions ● Machine Learning can allow us to make intelligent apps. ● It's a trendy topic… ● New ML platforms are emerging, allowing any developer to incorporate ML technologies.

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  • tuxbuddy

    May. 10, 2016
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    Jan. 27, 2017
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    Jun. 7, 2017

In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.

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