Machine Learning: Artificial Intelligence isn't just a Science Fiction topic
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The document provides an overview of machine learning and artificial intelligence, detailing key concepts such as definitions, algorithms, and applications. It emphasizes the importance of cloud-based machine learning platforms like Microsoft Azure ML and MonkeyLearn, which facilitate the development of intelligent applications. Additionally, it outlines the advantages of using machine learning to solve complex problems and improve user experience.
Machine Learning: Artificial Intelligence isn't just a Science Fiction topic
- 1. Machine Learning: Artificial Intelligence isn't just a Science Fiction topic Raul Garreta - Tryolabs / MonkeyLearn
- 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. Contents ● Brief intro to AI & Machine Learning (ML) ● ML Applications ● Cloud ML tools
- 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. 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. Different fields within AI Artificial Intelligence ● General Artificial Intelligence ● Expert Systems ○ Natural Language Processing ○ Computer Vision ○ Machine Learning ○ ...
- 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. 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. 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. 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. 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. Type of Machine Learning Algorithms Decision TreesLinear Models
- 13. Type of Machine Learning Algorithms Probabilistic / Statistical Models Neural Networks / Deep Learning
- 15. Important Concepts in ML Besides the Machine Learning… ● Data gathering / importation ● Data preprocessing ● Feature extraction ● Feature selection ● Performance evaluation (testing)
- 16. Applications Natural Language Processing Text Mining Speech to Text
- 18. Applications Data Mining / Predictive Analytics Recommendation Engines Medicine
- 19. Applications Intelligent Agents Robotics Game Players
- 20. 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...
- 21. ● 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?
- 22. As with other problems (eg: payments, communications) is a trend to go SaaS. Machine Learning Platforms Machine Learning
- 23. 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
- 24. 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
- 26. MonkeyLearn ● http://monkeylearn.com/ ● Launched private alpha on April 2014 ● Cloud based, focused on Text Mining: extract and classify information from text.
- 27. MonkeyLearn ● Easy to use. ● Pre-trained modules for different applications. ● GUI and APIs. ● More oriented to developers. ● Pricing: freemium, pay as you go.
- 29. 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.