Simulation: The Challenge for Data Science
While machine learning has recently had dramatic successes, there is a large class of problems that it will never be able to address on its own. To test a policy proposal, for example, often requires understanding a counterfactual scenario that has never existed in the past, and that may
From playlist Turing Seminars
Causal Behavioral Modeling Framework - Discrete Choice Modeling of Consumer Demand
There are increasing demands for "causal ML models" of the agent behaviors, which enable us to unbox the complex black-box models and make inferences or do proper counterfactual simulations. Many applications (especially in Marketing) intrinsically call for measurement of the causal impact
From playlist Fundamentals of Machine Learning
A solar system, a simulation made with Excel
An Excel simulation of the solar system. You can see how things are recursively computed: the mutual gravity force from the locations, the accelerations, the velocities, and finally the updated locations. The solar eclipse is also shown. This is clip is intended to illustrate Chapter 24 Ap
From playlist Physics simulations
The Explainer: What is a Business Model?
"Business model" and "strategy" are among the most sloppily used terms in business. --------------------------------------------------------------------- At Harvard Business Review, we believe in management. If the world’s organizations and institutions were run more effectively, if our
From playlist The Explainer
Simulating in Real Time: Hydraulic Actuator
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Configure multiple, independent solvers to enable real-time simulation. The model of a hydraulic aileron actuation system is simulated on a real-time target. For more video
From playlist Physical Modeling
Monte Carlo Simulation and Python 2 - Dice Function
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In this video, we begin creating our "roll dice" function. In the monte carlo simulation with Python series, we test various betting strategies. A
From playlist Monte Carlo Simulation with Python
Stanford Webinar: Business Models for Entrepreneurs and Innovators
http://create.stanford.edu/ This discussion with Professor Haim Mendelson explores the best approach for putting together a business model and how to use it for new business development opportunities. Learn why the business model is a blueprint for planning, and then building, new busine
From playlist Stanford Webinars
Efficient Robot Skill Learning via Grounded Simulation Learning, Imitation Learning... - Peter Stone
Seminar on Theoretical Machine Learning Topic: Efficient Robot Skill Learning via Grounded Simulation Learning, Imitation Learning from Observation, and Off-Policy Reinforcement Learning Speaker: Peter Stone Affiliation: The University of Texas at Austin Date: July 30, 2020 For more vide
From playlist Mathematics
Michael Hyland: "Integrating State-of-the-Art Mobility-on-Demand Fleet Models into Transportatio..."
Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop III: Large Scale Autonomy: Connectivity and Mobility Networks "Integrating State-of-the-Art Mobility-on-Demand Fleet Models into Transportation System Simulation Tools for Policy Analysis" Michael Hyland - Uni
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Carlo Tree Search
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
DeepMind x UCL RL Lecture Series - Planning & models [8/13]
Research Engineer Matteo Hessel explains how to learn and use models, including algorithms like Dyna and Monte-Carlo tree search (MCTS). Slides: https://dpmd.ai/planningmodels Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Simulating the economy as we simulate the climate... - Pollitt - Workshop 3 - CEB T3 2019
Pollitt (Cambridge Econometrics) / 04.12.2019 Simulating the economy as we simulate the climate: why most economists get it wrong ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/I
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Monte Carlo Simulation and Python
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 Here we bring at least the initial batch of tutorials to a close with the 3D plotting of our variables in search for preferable settings to use.
From playlist Monte Carlo Simulation with Python
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code t
From playlist Introduction to Deep Learning
Lecture 07: Planning and Learning with Tabular Methods
Seventh lecture video on the course "Reinforcement Learning" at Paderborn University during the summer term 2020. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials
From playlist Reinforcement Learning Course: Lectures (Summer 2020)
Why Choose Model-Based Reinforcement Learning?
What is the difference between model-free and model-based reinforcement learning? Explore the differences and results as the learning models are applied to balancing a cart/pole system as an example. By the end, you will have a better understanding of situations where you may want to choos
From playlist Reinforcement Learning
MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
MuZero harnesses the power of AlphaZero, but without relying on an accurate environment model. This opens up planning-based reinforcement learning to entirely new domains, where such environment models aren't available. The difference to previous work is that, instead of learning a model p
From playlist Reinforcement Learning
Monte Carlo Simulation and Python 10 -Analyzing some results
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In the monte carlo simulation with Python series, we test various betting strategies. A simple 50/50 strategy, a martingale strategy, and the d'ale
From playlist Monte Carlo Simulation with Python
Lecture 17 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. This course provides a
From playlist Lecture Collection | Machine Learning