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Date & Time: Tuesday, 12 February,
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Date & Time: Tuesday, 12 February,
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Date & Time: Wednesday, 13 February,
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ICTS-TIFR: An Overview
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ICTS and its Mandate
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The ICTS Campus - Imagined 2012
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The ICTS Campus - Realised 2017
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ICTS Research - Structure
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ICTS Programs
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What ICTS is Not
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ICTS Programs - Format
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ICTS Programs - Duration
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ICTS Programs - Organisation
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ICTS Programs - Directions
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ICTS Programs - Numbers
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ICTS Programs - A Sampling
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ICTS Outreach - Initiatives
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ICTS Outreach-Kaapi with Kuriosity
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Thank You See You Again at ICTS
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What is machine learning and deep learning?
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Machine learning ML: A new kind of science
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Talk overview
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Part 1 - Mathematical formalization of Machine Learning ML
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Old Idea: Curve fitting Legendre, Gauss, c. 1800
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Example: Learning to score reviews
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Example: Learning to rate reviews contd
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ML ~ finding suitable function "model" given examples of desired input/output behavior
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Formal framework
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Training via Gradient Descent "natural algorithm"
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Subcase: deep learning* deep models = "multilayered"
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Summary so far:
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Unsupervised learning no human-supplied labels
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A Language model baby "word2ver" [Mikolov et al'1 3]
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Properties of semantic word vectors
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Sequential decision-making framework
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Game-playing via Deep Learning crude account of Alpha-Go Zero
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Part 3 - Toward mathematical understanding of Deep Learning
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Special case: deep learning deep = "multilayered"
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Some key questions
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Analysis of optimization
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Black box analysis sketch
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More about optimization in next talk, including recent works using trajectory analysis for gradient descent
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Why no overfitting?
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Part 4 - Taking stock, wrapping up
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1. Imitation approach has not worked well in the past: airplanes, chess/go etc.
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Sample Task: "Decoding" Brain fMRI [Vodrahalli et al, Neurolmage'17]
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Brain regions useful for decoding
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Can Machine Learning thrive in India?
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Concluding thoughts on ML
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Q&A
Description:
Explore the foundations of machine learning in this comprehensive lecture by Sanjeev Arora from Princeton University. Delve into the mathematical formulations of various learning types, including supervised, unsupervised, and interactive learning. Gain insights into the philosophical and scientific issues surrounding machine learning. Examine key concepts such as curve fitting, gradient descent, and deep learning models. Discover applications in natural language processing, game playing, and brain fMRI decoding. Investigate the challenges of optimization and overfitting in deep learning. Conclude with a discussion on the future of machine learning and its potential impact in India. Engage in a thought-provoking Q&A session to further enhance your understanding of this rapidly evolving field.

Mathematics of Machine Learning: An Introduction - Lecture 1

International Centre for Theoretical Sciences
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#Computer Science #Machine Learning #Deep Learning #Supervised Learning #Unsupervised Learning #Reinforcement Learning #Artificial Intelligence #Neural Networks #Gradient Descent #Word Embeddings
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Date & Time: Tuesday, 12 February,