Introduction to Machine Learning
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This presentations introduces the basic concepts of machine learning and show examples of real world applications that are generating revenue.
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Introduction to Machine Learning
- 4. NVidia Perspective: Based on History My Perspective MACHINE INTELLIGENCE MACHINE LEARNING DEEP LEARNING Note: Slide design proposed by ML
- 6. Source: Borrowed heavily from Geoff Hinton’s Coursera course
- 7. TwoTypes of Supervised Learning Source: Borrowed heavily from Geoff Hinton’s Coursera course Note: Slide design proposed by ML
- 9. Source: Borrowed heavily from Geoff Hinton’s Coursera course
- 10. Source: Borrowed heavily from Geoff Hinton’s Coursera course
- 12. The branch of computer science concerned with making computers behave like humans. Field of study that gives computers the ability to learn without being explicitly programmed. The speed with which computers can carry out data or mathematical operations. Using case, if-then-else, or other software structures to render human knowledge in computer code. In the context of machine learning, algorithms define specific machine learning approaches (e.g., logistic regression, neural networks, classification trees). Methods for minimizing or maximizing some objective function. In machine learning, optimization is most often used to minimize some measure of error. Artificial Intelligence Machine Learning Compute Power Explicit Programming Algorithm Optimization
- 13. • Reviewing phone calls for quality or compliance • Reviewing transactions for fraud • Confirming government-issued IDs • Answering common customer questions • User login data where intrusion detection is a concern • Server log information that could lead to proactive production support • Identifying customer segments Human-like processing, where our scale is limited by headcount Situations where data is large or complicated, making it difficult for humans to find patterns
- 14. Talent / Capabilities to deliver results Machine Learning Leader Key RequirementsTo Succeed in ML Organization and Culture to drive overall effort Technology to enable ML projects to succeed • Vision • Leadership buy-in • ML dedicated teams • Experienced ML staff • Knowledge sharing • Data pipeline • Computing platforms
- 19. “Original” machine learning algorithm in which values are predicted or estimated Classification algorithm that was foundational to IBS ... And still is; high interpretability satisfies fair lending requirements Creating new variables from raw data to improve ML performance – can be manual or automated Algorithm (usually classification) that uses trees in a “serial” configuration; easily captures interactions and nonlinearities Algorithms (usually classification) that uses independent trees in a “parallel” configuration; powerful and easily parallelizable Algorithms that link multiple layers of inputs and outputs together with activation nodes; most often used in classification Collection of tools that, together, allow for extraction of meaning, topics, or sentiment from spoken or written language Linear Regression Logistic Regression Feature Engineering Gradient BoostedTrees Random Forrest (Bagged Trees) Neural Networks / Deep Learning NLP
- 27. AKA
- 30. Qurious.io: Improving performance of sales people
- 33. Textio.com:Writing better job descriptions / new ML text editor
- 34. Deep Genomics: Drug discovery and precision medicine
Editor's Notes
- Why does it work? Implications for coding – easy to steal? Can you replicate easily? Good for changing input, within same pattern. Compare regex to ML based approach When data changes, ‘easy’ to retrain with new data.
- Early approaches were based on rules based or expert systems. It is was extremely hard to quantify. What was called Artificial Intelligence then, is regarded as Artificial General intelligence (AGI) now. This can be contrasted with narrow intelligence. Machine Intelligence is different from human intelligence. Building a system that mimics human general intelligence is not seeming very practical at the moment, and possible of questionable value. Machines can be good at complementary things or subset of human attributes. We will come back to this at the end of the presentation In my own view, Deliberately put machine learning to occupy almost the whole spectrum of machine intelligence. There maybe very few activities contributing to intelligence outside of machine learning
- And I am trying to be provocative here. These three are not the same thing. Consider whether you need an analytics
- One trick here is to cast everything as a classification problem
- Non linear dimensionality reduction vs linear dimensionality reduction (ICA, PCA)
- Presenter notes/script: Each one of these can consume several classes and are the subject of many research projects.
- Presenter notes/script:
- Presenter notes/script: Each one of these can consume several classes and are the subject of many research projects.
- If we are a tech company, we care Unfair advantage of data