Probability and Prejudice: Bridging the Gap Between Machine Learning and Programming Languages

Channel:
Microsoft Research
Subscribers:
343,000
Published on ● Video Link: https://www.youtube.com/watch?v=38bf6vHx6vg


Duration: 55:46
128 views
5

Probabilistic programming languages are often thought of as a point of intersection between machine learning and programming languages. While this is true in some respects, they mostly occupy a gap between the two research areas. Technically, correctly running all probabilistic programs requires different algorithms, formalisms and theory than than are commonly used in either area. Culturally, the gap is perhaps wider: each area has its own vocabulary, ideals, standards of rigor, and motivations, which are often at odds. In this talk, I'll describe how I crossed the gap from machine learning to programming languages, and how I'm building a bridge back to the other side.



Other Videos By Microsoft Research


2016-06-21Computer Vision - StAR Lecture Series: Object Recognition
2016-06-21Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
2016-06-21An Introduction to Concentration Inequalities and Statistical Learning Theory
2016-06-21Intelligible Machine Learning Models for HealthCare
2016-06-21An evolutionary view on information processing in cells
2016-06-21Online Learning and Bandits - Part 1
2016-06-21X1-Locally Non-linear Embeddings for Extreme Multi-label Learning
2016-06-21Large-scale Linear and Kernel Classification - Part 2
2016-06-21Submodular Optimization and Machine Learning - Part 1
2016-06-21Large-scale Linear and Kernel Classification - Part 1
2016-06-21Probability and Prejudice: Bridging the Gap Between Machine Learning and Programming Languages
2016-06-21Stan Future Plans
2016-06-21Shogun Future Plans
2016-06-21Submodular Optimization and Machine Learning - Part 2
2016-06-21Active Learning and Annotation
2016-06-21Vowpal Wabbit Future Plans
2016-06-21Fast and Simple Algorithms for Constrained Submodular Maximization
2016-06-21Manifold correspondence: a signal processing perspective
2016-06-21Distributed Machine Learning Algorithms: Communication-Computation Trade-offs - Part 1
2016-06-21Reinforcement Learning: An Introduction
2016-06-21MSR India Research Overview


Tags:
microsoft research
machine learning
deep neural networks