(ML 13.3) Directed graphical models - formalism (part 1)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
(ML 13.4) Directed graphical models - formalism (part 2)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
generative model vs discriminative model
understanding difference between generative model and discriminative model with simple example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
Walking Robot Mechanism 3D Model
A simple walking machine, used to teach kinematics. It uses 4-bars mechanisms to create the movements. Free 3D model at https://skfb.ly/onQMo.
From playlist Walking Machines
Four-Legs Walking-Machine 3D Model
Based on this site: (http://www.armure.ch/WALKING.htm). Modeled with Solidworks 2015. Rendered with Simlab Composer 7 Mechanical Edition.
From playlist Walking Machines
(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
A simple gears mechanism moving a frame structure. Used in robot toys. Free 3D model at https://skfb.ly/o6X7q.
From playlist Walking Machines
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
Phase transitions in hard-core systems by Deepak Dhar ( Lecture - 1 )
PROGRAM BANGALORE SCHOOL ON STATISTICAL PHYSICS - X ORGANIZERS : Abhishek Dhar and Sanjib Sabhapandit DATE : 17 June 2019 to 28 June 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the tenth in the series. This is a pedagogical school, aimed at bridgin
From playlist Bangalore School on Statistical Physics - X (2019)
Some Theoretical Results on Model-Based Reinforcement Learning by Mengdi Wang
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE & TIME 04 January 2021 to
From playlist Advances in Applied Probability II (Online)
Multiple Phase Transitions in a System of Hard Core Rotors on a Lattice (Lecture 1) by Deepak Dhar
INFOSYS-ICTS CHANDRASEKHAR LECTURES MULTIPLE PHASE TRANSITIONS IN A SYSTEM OF HARD CORE ROTORS ON A LATTICE SPEAKER: Deepak Dhar (Distinguished Emeritus Professor and NASI-Senior Scientist, IISER-Pune, India) VENUE: Ramanujan Lecture Hall and Online DATE & TIME: Lecture 1: Monday, D
From playlist Infosys-ICTS Chandrasekhar Lectures
Current fluctuations and dynamical phase transitions by Rosemary J Harris
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Stateflow Quick Start for Student Competition Teams
Learn basic Stateflow® terminology and functionality, as well as the workflow to design and simulate a simple state diagram. Stateflow is a graphical environment that enables modeling and simulating decision logic using state machines and flow charts. For more information about Stateflow
From playlist Robotics Education: MATLAB and Simulink Robotics Arena
Phase transitions and symmetry breaking in current distributions of diffusive systems by Yariv Kafri
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Thermal properties of frustrated quantum magnets by Frederic Mila
PROGRAM FRUSTRATED METALS AND INSULATORS (HYBRID) ORGANIZERS: Federico Becca (University of Trieste, Italy), Subhro Bhattacharjee (ICTS-TIFR, India), Yasir Iqbal (IIT Madras, India), Bella Lake (Helmholtz-Zentrum Berlin für Materialien und Energie, Germany), Yogesh Singh (IISER Mohali, In
From playlist FRUSTRATED METALS AND INSULATORS (HYBRID, 2022)
Machine learning methods trained on simple models can predict critical transitions... by Smita Deb
PROGRAM TIPPING POINTS IN COMPLEX SYSTEMS (HYBRID) ORGANIZERS Partha Sharathi Dutta (IIT Ropar, India), Vishwesha Guttal (IISc, India), Mohit Kumar Jolly (IISc, India) and Sudipta Kumar Sinha (IIT Ropar, India) DATE & TIME 19 September 2022 to 30 September 2022 VENUE Ramanujan Lecture Hal
From playlist TIPPING POINTS IN COMPLEX SYSTEMS (HYBRID, 2022)
Continuous Mott transitions in a model Hamiltonian system by N S Vidhyadhiraja
29 May 2017 to 02 June 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore This program aims to bring together people working on classical and quantum systems with disorder and interactions. The extensive exploration, through experiments, simulations and model calculations, of growing cor
From playlist Correlation and Disorder in Classical and Quantum Systems
Non-stationary Markow Processes: Approximations and Numerical Methods by Peter Glynn
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative
From playlist Research Abstracts from Brunton Lab