4th International Conference On Recent Advances in Mathematical Sciences and Applications (RAMSA - 21) organized by GVP College of Engineering. This deck is an overview of the trends in ML Engineering which is evolving as a discipline and how Mathematics, Machine Learning and ML Engineering are related to one another.
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Mathematics, Machine Learning and ML Engineering
1. Mathematics, Machine Learning
and ML Engineering
Gopi Krishna Nuti
Lead Data Scientist, Autodesk
Vice President, MUST Research
Ngopikrishna.public@gmail.com
3. Select milestones
Year Caption
1805 Least 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
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
7. ML/AI
Engineering
A truly multi-disciplinary field
Combines Computer Science
and Cognitive Science
Potentially transformative
applications in many areas of
science, industry and society.
10. AutoML
• An amalgamation of 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
17. Where does everyone fit?
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
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.