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Mathematics, Machine Learning and ML Engineering
The document outlines the milestones in mathematics and machine learning from historical algorithms to recent advancements. It discusses the role of mathematics in machine learning and the emergence of auto ML tools that democratize access to AI while raising concerns about bias and transparency. Additionally, it highlights the interdisciplinary nature of ML engineering and its transformative impact across various domains.
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Mathematics, Machine Learning and ML Engineering
- 1. Mathematics, Machine Learning andML Engineering Gopi Krishna Nuti Lead Data Scientist, Autodesk Vice President, MUST Research Ngopikrishna.public@gmail.com
- 2. Machine Learning –Select Milestones •Linear Regression 1805 •Neural Networks 1943 •K-NN 1951 •Perceptron •Logistic Regression 1958 •Support Vector Machine 1963 •K-Means Clustering 1967 •Decision Trees 1968 •RNN 1986 •LeNet 1990 •Random Forest 1995 •LSTM 1997 •GANs 2014 Supervised Learning - Regression Supervised Learning - Classification Neural Networks and Deep Learning Unsupervised Learning Content creation
- 3. Select milestones Year Caption 1805Least Square 1812 Bayes' Theorem 1913 Markov Chains 1950 Turing's Learning Machine 1951 First Neural Network Machine 1952 Machines Playing Checkers 1957 Perceptron 1967 Nearest Neighbor 1970 Automatic Differentiation (Backpropagation) 1976 Transfer Learning 1982 Recurrent Neural Network 1986 Backpropagation 1989 Reinforcement Learning 1995 Random Forest Algorithm 1995 Support Vector Machines 1997 LSTM 2005 RankNet Year Caption 2014 Leap in Face Recognition 2014 Sibyl 2014 XgBoost 2014 GANs 2014 Regions with CNN features 2015 Fast R-CNN and Faster R-CNN, Inception V3 2016 YOLO, SSD 2017 Google AI – Attention is all you need 2018 ULMfit fast.ai 2018 BERT 2019 Stanford NLP 2019 Open AI GPT2 2020 Open AI GPT3 beta
- 4. Do we need Mathematicsfor Machine Learning?
- 5. Impact of Mathematics on AI/ML •Classical Machine Learning algorithms • Concepts like p-Values, power of a hypothesis, • Data distributions (Normal, F, T etc), • Test statistics like Chi-Squaer Statistics • Some classical Machine Learning algorithms • Distance between points, orthogonal coordinates etc. Coordinate Geometry • Neural Networks • Perceptron • Differentiations, Integrations and more… Linear Algebra
- 6. Recent mathematical concepts in AI/ML Ranking LSTMs, GANsetc. XgBoost Spiking Neural Networks And more
- 7. ML/AI Engineering A truly multi-disciplinaryfield Combines Computer Science and Cognitive Science Potentially transformative applications in many areas of science, industry and society.
- 8. Do we need Mathematicsfor ML Engineering That’s the question, isn’t it?
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- 10. AutoML • An amalgamationof Data Science algorithms made possible by best practices of Programming • Machine Learning models are available at the click of a button. • Sometimes, even drag-n-drop is not necessary. • Good - Democratizes AI. Citizen data scientists • Bad – Black boxes. • Ugly - Invisible Bias
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- 12. Is this desirable Picturecourtesy: https://www.thoughtco.com/thomas-edison-1779841
- 13. Has this happenedbefore?
- 14. Trends in MLEngineering Multi dimensional New algorithms New domains Commoditiz ation Evolution as a discipline
- 15. Gartner’s AI HypeCycle for 2020 Image courtesy Gartner.com
- 16. AI Services Commoditization Imagecourtesy : Gartner.com
- 17. Where does everyonefit? ML Researcher Classical Approaches • How can statistics be used? Deep Learning • 3rd and 4th gen Neural Networks Interpretability of models ML Engineer Commoditize Standardize Expand to new domains
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- 19. Applying AI for Math •Applying AI for long standing problems for “near optimal” solutions. • Examples • Travelling Salesman Problem • Navier Stokes Equations • Proving Kazhdan-Lusztig polynomials • Supervised learning for identifying a previously unknown relationship between two knots in Knot theorem • Discovering new patterns in Pure Mathematics https://www.sciencealert.com/ai-is-discovering-patterns-in-pure-mathematics-that-have-never-been- seen-before
- 20. Applying Math for AI •Interpretability of black box models • Spiking Neural Networks • Identifying the functions that govern vision, hearing etc.
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