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


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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 ● 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 ● ● 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.