21. Bayesian Statistical Inference I
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis ...
MIT OpenCourseWare
Data Science - Part XIII - Hidden Markov Models
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations ...
Derek Kane
Professor Mark Girolami: "Probabilistic Numerical Computation: A New Concept?"
The Turing Lectures: The Intersection of Mathematics, Statistics and Computation - Professor Mark Girolami: "Probabilistic Numerical Computation: A New ...
The Alan Turing Institute
Introduction to Bayesian statistics, part 1: The basic concepts
An introduction to the concepts of Bayesian analysis using Stata 14. We use a coin toss experiment to demonstrate the idea of prior probability, likelihood ...
StataCorp LLC
Lecture 04 - Error and Noise
Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 of 18 of Caltech's Machine ...
caltech
Lecture 9: Machine Translation and Advanced Recurrent LSTMs and GRUs
Lecture 9 recaps the most important concepts and equations covered so far followed by machine translation and fancy RNN models tackling MT. Key phrases: ...
Stanford University School of Engineering
Jonathan Sedar - Hierarchical Bayesian Modelling with PyMC3 and PySTAN
PyData London 2016 Can we use Bayesian inference to determine unusual car emissions test for Volkswagen? In this worked example, I'll demonstrate ...
PyData
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/2YdUtfp Professor ...
stanfordonline
Deep Learning 1: Introduction to Machine Learning Based AI
Thore Graepel, Research Scientist shares an introduction to machine learning based AI as part of the Advanced Deep Learning & Reinforcement Learning ...
DeepMind
Jackie Kazil | Agent based modeling in Python
PyData DC 2016 Agent-based modeling is a technique used to explore both complexity and emergence by simulating individual actors and their actions within a ...
PyData
undergraduate machine learning 8: Inference in Bayesian networks and dynamic programming
Inference for Bayesian networks, aka probabilitistic graphical models. Dynamic programming. The slides are available here: ...
Nando de Freitas
A DARPA Perspective on Artificial Intelligence
What's the ground truth on artificial intelligence (AI)? In this video, John Launchbury, the Director of DARPA's Information Innovation Office (I2O), attempts to ...
DARPAtv
Map Matching @ Uber
At a recent Uber Marketplace meetup, software engineer Karan Parikh discusses how Uber Engineering leverages the Hidden Markov Model to improve our ...
Uber Engineering
4. Stochastic Thinking
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag ...
MIT OpenCourseWare
RI Seminar: Stefan Schaal : From Movement Primitives to Associative Skill Memories
Stefan Schaal Professor of Computer Science, Neuroscience, and Biomedical Engineering, University of Southern California March 28, 2014 Abstract ...
cmurobotics
Thomas Huijskens - Bayesian optimisation with scikit-learn
Filmed at PyData London 2017 Description Join Full Fact, the UK's independent factchecking charity, to discuss how they plan to make factchecking dramatically ...
PyData
15. Unraveling Open System Quantum Dynamics
MIT 8.422 Atomic and Optical Physics II, Spring 2013 View the complete course: http://ocw.mit.edu/8-422S13 Instructor: Wolfgang Ketterle In this lecture, the ...
MIT OpenCourseWare
Ranking Systems: Elo, TrueSkill and Your Own
In this 2017 GDC talk, Twitch's Mario Izquierdo exposes the math behind standardized ranking systems like Elo and TrueSkill to help you develop your own ...
GDC
The Data-Driven Prognosis: Creating Predictive Models to Maximize Clinical System Availability
Mauro Barbieri, Phillips Research Toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data The Data-Driven Prognosis: Creating ...
SiliconANGLE theCUBE
MIA: Joshua Weinstein, Optics-free spatio-genetic imaging with DNA microscopy
February 2nd, 2019 Joshua Weinstein Zhang Lab, Regev Lab Optics-free spatio-genetic imaging with DNA microscopy Abstract: Complex cell populations, from ...
Broad Institute
Conversation about drug pricing | Health care system | Heatlh & Medicine | Khan Academy
Dr. Laurence Baker talks to Sal about how drug prices are set. Created by Sal Khan. Watch the next lesson: ...
Khan Academy
Restricted Boltzmann Machine | Neural Network Tutorial | Deep Learning Tutorial | Edureka
AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka video on "Restricted Boltzmann ...
edureka!
Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka
Machine Learning Training with Python: https://www.edureka.co/data-science-python-certification-course ** This Machine Learning Interview Questions and ...
edureka!
Building a Fraud Detection Platform using AI and Big Data
Learn more about AWS Startups at – https://amzn.to/2CXQYy2 Yuaho Zheng, Director of Engineering at DataVisor, talks about building a fraud detection platform ...
Amazon Web Services
Artificial Intelligence: How It Works and What It Means for the Future - Yisong Yue - 1/13/2021
Over the past decade, artificial intelligence (AI) and the massive amounts of data powering such systems have dramatically changed our world. And as both the ...
caltech
RI Seminar: Michael Kaess: Factor Graphs for Robot Perception
https://www.ri.cmu.edu/event/ri-seminar-michael-kaess-cmu-2018-09-21/ Michael Kaess Assistant Research Professor Robotics Institute, Carnegie Mellon ...
cmurobotics
"Finding bugs without running or even looking at code" by Jay Parlar
What if you could find complex bugs in systems without ever having looked at any of the code, without running the code, without cloning the code, or even ...
Strange Loop
Quantum Reality: Space, Time, and Entanglement
Brian Greene moderates this fascinating program exploring the fundamental principles of Quantum Physics. Anyone with an interest in science will enjoy this ...
World Science Festival
Dr Jessica Stauth: Portfolio and Risk Analytics in Python with pyfolio | PyData NYC 2015
Dr Jessica Stauth: Portfolio and Risk Analytics in Python with pyfolio PyData NYC 2015 Pyfolio is a recent open source library developed by Quantopian to ...
PyData
Моделирование систем. Лекция 6. Модели теории игр и марковские модели
1. Марковский случайный процесс. 2. Потоки событий. 3. Уравнения Колмогорова для вероятностей состояний. 4. Предмет и задачи теории игр. 5.
Образование для всех
Webinar: Musculoskeletal Models Deconstructed
Musculoskeletal models provide a non-invasive means to study human movement in greater detail than can typically be done in a laboratory setting. Interpreting ...
OpenSimVideos
Bayesian Network
Machine Learning- Sudeshna Sarkar
Загадка сна — нужно ли её разгадывать? | Михаил Полуэктов | TEDxPokrovkaSt
Мечта или сон? Английское dream в русской интерпретации имеет сразу два толкования. Сон — важная составляющая жизни каждого из нас, а потому ...
TEDx Talks
4. Blockchain Basics & Consensus
MIT 15.S12 Blockchain and Money, Fall 2018 Instructor: Prof. Gary Gensler View the complete course: https://ocw.mit.edu/15-S12F18 YouTube Playlist: ...
MIT OpenCourseWare
17. Markov Chains II
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis ...
MIT OpenCourseWare
Addinsoft Conference 2021
As part of its 20th anniversary, Addinsoft, the maker of XLSTAT, will present new products, new partnerships and the latest XLSTAT improvements. -- 1st Part ...
XLSTAT
MIT AGI: Artificial General Intelligence
This is the opening lecture for course 6.S099: Artificial General Intelligence. This class is free and open to everyone. Our goal is to take an engineering approach ...
Lex Fridman
Introduction to Sensor Fusion for Autonomous Vehicle (Part - 2) | Skill-Lync | Workshop
In this video of the introduction to sensor fusion for autonomous vehicles, our instructor talks about the current trends in the state of the art perception technology ...
Skill Lync
Jared Simpson | Analysis Tools for Nanopore Data
Flongle, GridION, MinION, MinIT, PromethION, and VolTRAX are currently for research use only.
Oxford Nanopore Technologies
Peter Thiel on the Global Economy, the State of Our Technology, and Artificial Intelligence
The business founder and investor discusses America, globalization, and the state of technological innovation. Click "Show more" to view all chapters. For more ...
Conversations with Bill Kristol
Harnessing Finance in an Age of Environmental Breakdown | SOAS University of London
Harnessing Finance in an Age of Environmental Breakdown: Rethinking the Role of Financial Authorities was recorded with Katie Kedward (UCL) and Gregor ...
SOAS University of London
How Understanding Probability Helps Us Make Better Decisions with Mehran Sahami
This talk was a part of Classes Without Quizzes at Reunion Homecoming 2016. As we make daily decisions, many people base them on initial reactions or gut ...
Stanford Alumni
