Completed
ML ~ finding suitable function "model" given examples of desired input/output behavior
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Mathematics of Machine Learning: An Introduction - Lecture 1
Automatically move to the next video in the Classroom when playback concludes
- 1 Date & Time: Tuesday, 12 February,
- 2 Date & Time: Tuesday, 12 February,
- 3 Date & Time: Wednesday, 13 February,
- 4 ICTS-TIFR: An Overview
- 5 ICTS and its Mandate
- 6 The ICTS Campus - Imagined 2012
- 7 The ICTS Campus - Realised 2017
- 8 ICTS Research - Structure
- 9 ICTS Programs
- 10 What ICTS is Not
- 11 ICTS Programs - Format
- 12 ICTS Programs - Duration
- 13 ICTS Programs - Organisation
- 14 ICTS Programs - Directions
- 15 ICTS Programs - Numbers
- 16 ICTS Programs - A Sampling
- 17 ICTS Outreach - Initiatives
- 18 ICTS Outreach-Kaapi with Kuriosity
- 19 Thank You See You Again at ICTS
- 20 What is machine learning and deep learning?
- 21 Machine learning ML: A new kind of science
- 22 Talk overview
- 23 Part 1 - Mathematical formalization of Machine Learning ML
- 24 Old Idea: Curve fitting Legendre, Gauss, c. 1800
- 25 Example: Learning to score reviews
- 26 Example: Learning to rate reviews contd
- 27 ML ~ finding suitable function "model" given examples of desired input/output behavior
- 28 Formal framework
- 29 Training via Gradient Descent "natural algorithm"
- 30 Subcase: deep learning* deep models = "multilayered"
- 31 Summary so far:
- 32 Unsupervised learning no human-supplied labels
- 33 A Language model baby "word2ver" [Mikolov et al'1 3]
- 34 Properties of semantic word vectors
- 35 Sequential decision-making framework
- 36 Game-playing via Deep Learning crude account of Alpha-Go Zero
- 37 Part 3 - Toward mathematical understanding of Deep Learning
- 38 Special case: deep learning deep = "multilayered"
- 39 Some key questions
- 40 Analysis of optimization
- 41 Black box analysis sketch
- 42 More about optimization in next talk, including recent works using trajectory analysis for gradient descent
- 43 Why no overfitting?
- 44 Part 4 - Taking stock, wrapping up
- 45 1. Imitation approach has not worked well in the past: airplanes, chess/go etc.
- 46 Sample Task: "Decoding" Brain fMRI [Vodrahalli et al, Neurolmage'17]
- 47 Brain regions useful for decoding
- 48 Can Machine Learning thrive in India?
- 49 Concluding thoughts on ML
- 50 Q&A
