"Explainable Machine Learning Models for Healthcare AI"
Title: Explainable Machine Learning Models for Healthcare AI Speakers: Ankur Teredesai, Dr. Carly Eckert, Muhammad Aurangzeb Ahmad, and Vikas Kumar ...
Association for Computing Machinery (ACM)
A Bluffer's Guide to Dimension Reduction - Leland McInnes
PyData NYC 2018 Dimension reduction is a complicated topic with a vast zoo of diverse techniques for different specialised problems. This talk will seek to cut ...
PyData
Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/2rf9OO3 Professor ...
stanfordonline
IACS Seminar: "Machine Learning for Materials Discovery" 11/30
Presented by Dr. Julia Ling, Director of Data Science at Citrine Informatics Talk abstract: Materials science presents a unique set of challenges and opportunities ...
Harvard Institute for Applied Computational Science
Chest Imaging During the COVID-19 Pandemic: An Update
Following the April 2020 simultaneous publication in Radiology and Chest of the Fleischner Statement on the role of chest imaging in COVID-19, Dr. Geoffrey ...
RSNA
Dean Langsam: Disease Modeling with Scipy and PyMC | PyData Warsaw 2019
Programs which aim eradicate disease must rely on interpretable models. These models quickly become hard to solve, not to mention train on missing ...
PyData
Embeddings for Everything: Search in the Neural Network Era
Dean's lecture, with Dan Gillick — Retrieval systems like internet search still use the same underlying keyword-based index they used back in the 1990s.
Berkeley School of Information
FDA Advisory Committee Approves Pfizer’s COVID-19 Vaccine - BTN162b2
FDA Advisory Committee Approves Pfizer's COVID-19 Vaccine - BTN162b2 More lectures on drbeen.com Looking to support my educational work? Donate ...
Drbeen Medical Lectures
Vincent Warmerdam: Winning with Simple, even Linear, Models | PyData London 2018
PyData London 2018 Simple models work. Linear models work. No need for deep learning or complex ensembles, you can often keep it simple. In this talk I'll ...
PyData
A Machine Learning Approach to Optimize Prices During Clearance Sales at MANGO | MANGO
Download Slides: https://www.datacouncil.ai/talks/a-machine-learning-approach-to-optimize-prices-during-clearance-sales-at-mango WANT TO EXPERIENCE ...
Data Council
Machine Learning Algorithms: Understanding Decision Tree Algorithm and Random Forest Algorithm
In this session, Arihant Jain, Data Scientist at ZestMoney, explains two important machine learning algorithms - decision tree algorithm and random forest ...
Springboard India
4. Expectations, Momentum, and Uncertainty
MIT 8.04 Quantum Physics I, Spring 2013 View the complete course: http://ocw.mit.edu/8-04S13 Instructor: Allan Adams In this lecture, Prof. Adams begins with a ...
MIT OpenCourseWare
Using Machine Learning and Data Science to Solve Real Business Problems (DataEDGE 2018)
Sourav Dey, Managing Director of Machine Learning, Manifold — AI and machine learning have the power to transform entire industries. Companies in ...
Berkeley School of Information
John C. Havens interviews Professor Alex 'Sandy' Pentland for the IEEE SA Series, Measurementality
Professor Alex 'Sandy' Pentland speaks with John C. Havens of IEEE SA in the first official video interview for the Measurementality series, describing how our ...
IEEE Standards Association
Stanford Seminar - Information Theory of Deep Learning
EE380: Computer Systems Colloquium Seminar Information Theory of Deep Learning Speaker: Naftali Tishby, Computer Science, Hebrew Univerisity I will ...
stanfordonline
The Path From Cloud AutoML to Custom Model (Cloud Next '19)
What comes after AutoML? You've created some models in Cloud AutoML, and they've been useful. But you want to see if there's some room for more ...
Google Cloud Platform
Computational Imaging SPACE Webinar Series: Katie Bouman, Caltech
IEEE Signal Processing Society
AI vs Climate Change: Insights from the Cutting Edge (Google I/O'19)
DeepMind builds AI systems that can learn new skills and ideas, to help people solve problems that can seem impossible today - such as the urgent challenge of ...
Google Developers
Secure and compliant machine learning workflows with Amazon SageMaker
Ever wondered how to build a secure and compliant end-to-end ML workflow for Financial Services? In this video, we address the common patterns and ...
Amazon Web Services
Microsoft Research AI Breakthroughs 2020: 20 minute research talks, Q&A panel, and event wrap-up
20 minute research talks, Q&A panel, and event wrap-up 1:22 Shamsi Iqbal: Micro-productivity: Redefining Productivity to Adapt to a Changing Landscape of ...
Microsoft Research
Stanford Seminar - Deep Learning for Medical Diagnoses
Pranav Rajpurkar Stanford University April 17, 2019 The use of algorithms in clinical care demands a very high performance level for accurate detection and ...
stanfordonline
High Dimensional Data Visualization with Clustergrammer2 |SciPy 2020| Nicolas Fernandez
Visualizing complex, high-dimensional data is a key step in data analysis and is traditionally approached using dimensionality reduction techniques (e.g. t-SNE, ...
Enthought
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Free Control
Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for ...
stanfordonline
Eva van Weel, Fabian Jansen: What's the uncertainty on your ML Prediction | PyData Amsterdam 2019
Uncertainties are invaluable for decision making. Say you expect 10 people at your dinner party but it could easily be 8 people more, then the amount of food ...
PyData
Judea Pearl: Causal Reasoning, Counterfactuals, and the Path to AGI | Lex Fridman Podcast #56
Lex Fridman
Program Synthesis meets Machine Learning
We give a tutorial overview of program synthesis, from its first formulation by Church in 1957, through its pragmatic evolution through sketching and ...
Microsoft Research
Leland Mcinnes: Topological Techniques for Unsupervised Learning | PyData LA 2019
www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the ...
PyData
Lecture 12 | Visualizing and Understanding
In Lecture 12 we discuss methods for visualizing and understanding the internal mechanisms of convolutional networks. We also discuss the use of ...
Stanford University School of Engineering
Reprogramming the Human Genome: Why AI is Needed - Brendan Frey, CEO, Deep Genomics
Reprogramming the Human Genome: Why AI is Needed - Brendan Frey, CEO, Deep Genomics We have figured out how to write to the genome using DNA ...
RE•WORK
The Mind Gut Connection with Faith Dickerson, PhD, and Emeran Mayer, MD
Is your gut a second brain? Emerging research is showing that our brains and our gastrointestinal systems may be more connected than we previously thought ...
American Psychological Association
Stéphane Mallat - Multiscale Models for Image Classification and Physics with Deep Networks
Abstract: Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning, image processing, physics and ...
Institut des Hautes Études Scientifiques (IHÉS)
Zack Witten: Extracting Structured Data from Legal Documents | PyData LA 2018
PyData LA 2018 You'll learn how to take a never-before-seen legal document, like a contract or a convertible note, and use machine learning to “read” the ...
PyData
How AI is Transforming the Enterprise (Cloud Next '19)
AI is no magic pixie dust to sprinkle on your existing applications to make them “intelligent”. It is a use-case driven custom integration of key fundamental building ...
Google Cloud
SAS Tutorial | How to compare models in SAS
In this SAS How To Tutorial, Jeff Thompson shows how to compare models in SAS. When working to solve business problems, analysts and data scientist often ...
SAS Users
MIA: Mohammed AlQuraishi, End-to-end differentiable learning of protein structure
March 6, 2019 MIA Meeting Mohammed AlQuraishi HMS End-to-end differentiable learning of protein structure Abstract: Predicting protein structure from ...
Broad Institute
Emergent linguistic structure in deep contextual neural word representations - Chris Manning
Workshop on Theory of Deep Learning: Where next? Topic: Emergent linguistic structure in deep contextual neural word representations Speaker: Chris ...
Institute for Advanced Study
Dave Blei: "Black Box Variational Inference"
A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in ...
PROBPROG Conference
7. ChIP-seq Analysis; DNA-protein Interactions
MIT 7.91J Foundations of Computational and Systems Biology, Spring 2014 View the complete course: http://ocw.mit.edu/7-91JS14 Instructor: David Gifford In ...
MIT OpenCourseWare
Beyond word2vec: GloVe, fastText, StarSpace - Konstantinos Perifanos
PyData London 2018 Word embeddings is a very convenient and efficient way to extract semantic information from large collections of textual or textual-like data ...
PyData
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 - Adversarial Attacks / GANs
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University http://onlinehub.stanford.edu/ Andrew Ng Adjunct Professor, Computer ...
stanfordonline
[Uber Open Summit 2018] Pyro: Deep Probabilistic Programming
Pyro is a deep probabilistic programming language built on PyTorch, a GPU-accelerated deep learning framework. Developed at Uber AI Labs by Noah ...
Uber Engineering
Probabilistic Machine Learning and AI
How can a machine learn from experience? Probabilistic modelling provides a mathematical framework for understanding what learning is, and has therefore ...
Microsoft Research