Understanding Kalman Filters, Part 1: Why Use Kalman Filters?
Discover common uses of Kalman filters by walking through some examples. A Kalman filter is an optimal estimation algorithm used to estimate states of a ...
MATLAB
Optimal Control with Python GEKKO
An optimal control problem has differential equation constraints and is solved with Python GEKKO. The integral objective is minimized at the final time. 11 of 18 ...
APMonitor.com
L9.1 Stability margins for LQ-optimal regulators
In this video we will analyze the stability margins (gain margin and phase margin) of state-feedback control loop with LQ-optimal regulator. Graduate course on ...
aa4cc
StatQuest: Maximum Likelihood, clearly explained!!!
If you hang out around statisticians long enough, sooner or later someone is going to mumble "maximum likelihood" and everyone will knowingly nod. After this ...
StatQuest with Josh Starmer
Marcin Pilipczuk. Optimal Discretization is Fixed-parameter Tractable
Talks on Frontiers of Parameterized Complexity https://frontpc.blogspot.com Keywords: Optimal discretization, Red blue points, CSP, FPT June 25, 2020 Marcin ...
Frontiers of Parameterized Complexity
Codon optimization: Why & how to design DNA sequences for optimal soluble protein expression
Have you struggled with low protein expression levels in your experiments? This webinar will explain the principles of codon optimization and explore case ...
GenScript USA Inc.
Optimal Control Solved with Excel and Python GEKKO
A simple benchmark problem is used to demonstrate a dynamic optimization test from a benchmark set of singular optimal control problems.
APMonitor.com
Mod-16 Lec-37 Optimal Control of Distributed Parameter Systems -- I
Optimal Control, Guidance and Estimation by Dr. Radhakant Padhi, Department of Aerospace Engineering, IISc Bangalore. For more details on NPTEL visit ...
nptelhrd
Machine Learning Control: Genetic Algorithms
This lecture provides an overview of genetic algorithms, which can be used to tune the parameters of a control law. Machine Learning Control T. Duriez, S. L. ...
Steve Brunton
Die optimale Diät zum Fettverlust
95% aller Diätversuche scheitern … das ist ein trauriger Fakt. Gleichzeitig gibt es immer wieder neue Ansätze, neue vermeintliche Abkürzungen, neue ...
Sjard Roscher
KMeans clustering: Elbow Method C# Optimal number of clusters Source Code is included
The project is written from scratch in C#. Source code is also fully available on my blog or upon request. Blog: http://code-ai.mk/ Project Files: ...
Vanco Pavlevski
Optimizing C for Microcontrollers - Best Practices - Khem Raj, Comcast RDK
Optimizing C for Microcontrollers - Best Practices - Khem Raj, Comcast RDK This talk will cover the tips and techniques to write best possible C programs for ...
The Linux Foundation
Advanced Methods for Optimal Scheduling Using Gurobi
Scheduling problems arise in a wide range of applications, and solving large-scale problems efficiently can require expert knowledge and insights. In this ...
Gurobi Optimization
How to find the best model parameters in scikit-learn
In this video, you'll learn how to efficiently search for the optimal tuning parameters (or "hyperparameters") for your machine learning model in order to maximize ...
Data School
Bryson Denham Optimal Control
The Bryson-Denham optimal control problem is a benchmark test problem for optimal control algorithms. The parameter u (acceleration) is adjusted over the ...
APMonitor.com
Machine Learning Control: Tuning a PID Controller with Genetic Algorithms
This lecture shows how to use genetic algorithms to tune the parameters of a PID controller. Tuning a PID controller with genetic algorithms is not generally ...
Steve Brunton
FAST '15 - Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth
Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth KV Rashmi, Preetum Nakkiran, Jingyan Wang, ...
USENIX
Bryson Singular Optimal Control Problem
Dynamic programming or dynamic optimization can be used to solve optimal control problems such as the Bryson benchmark problem. This problem is solved ...
APMonitor.com
Speaker Diarization: Optimal Clustering and Learning Speaker Embeddings
Speaker diarization consist of automatically partitioning an input audio stream into homogeneous segments (segmentation) and assigning these segments to the ...
Microsoft Research
Data processing: Achieving optimal performance automatically (Google Cloud Next '17)
Performance is great, but what's even better than finely-tuned, benchmark-optimized systems? Performance that's geared towards your exact needs. In this video ...
Google Cloud Platform
Near-Optimal Strong Dispersers - Dean Doron
Computer Science/Discrete Mathematics Seminar I Topic: Near-Optimal Strong Dispersers Speaker: Dean Doron Affiliation: The University of Texas at Austin ...
Institute for Advanced Study
Franziska Hinkelmann: JavaScript engines - how do they even? | JSConf EU
http://2017.jsconf.eu/speakers/franziska-hinkelmann-javascript-engines-how-do-they-even.html Want to know how JavaScript engines work? Why is JavaScript ...
JSConf
Automated backtesting for optimal parameter for max profit
This is possible statement I can make with this source code that is available for download ...
Bryan Downing
Unity at GDC - Job System & Entity Component System
March 23, 11:00 am (San Francisco) - Learn about Unity 2018.1's new C# job system for game code, as well as a new (experimental) Entity Component System ...
Unity
Nearly Optimal Robust Secret Sharing against Rushing Adversaries
Paper by Pasin Manurangsi, Akshayaram Srinivasan, Prashant Nalini Vasudevan presented at Crypto 2020 See ...
TheIACR
SciPy Beginner's Guide for Optimization
Scipy.Optimize.Minimize is demonstrated for solving a nonlinear objective function subject to general inequality and equality constraints. Source code is ...
APMonitor.com
Optimal Predictive Control 4_8 - numerical examples and MATLAB
Uses MATLAB to illustrate the OMPC and SOMPC algorithms in the constraint free and regulation case. Both are guaranteed stable, however whereas OMPC ...
John Rossiter
A Bayesian Approach to Optimal Pricing Using Business Rules
Business rules have become an important part of the practice of price optimization systems. These rules are meant to capture managerial knowledge and ...
Hui Lin
Fuji Electric Frenic Mega Series AC Drive Basic Start-Up Using the Keypad
How to do a basic start-up and demo of the Fuji Electric Frenic Mega Series AC Drive Using the Keypad. Buy the item featured in this video at 800-337-1720 or ...
GalcoTV
Jorma Rissanen: Optimal Estimation
2009 ISIT Shannon Lecture Optimal Estimation Professor Jorma Rissanen University of Tampere Abstract: In this talk we give a common theory of estimation of ...
IEEE Information Theory Society
Microsoft Research AI Breakthroughs 2020: 20 minute research talks + Q&A panel
20 minute research talks + Q&A panel 0:59 Debadeepta Dey: Efficient Forward Neural Architecture Search 15:45 Subho Mukherjee: Resource-efficient Learning ...
Microsoft Research
18. Complexity: Fixed-Parameter Algorithms
MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Erik Demaine In this lecture, ...
MIT OpenCourseWare
Selecting the best model in scikit-learn using cross-validation
In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting ...
Data School
Luus Optimal Control Problem
Dynamic optimization is applied to numerically solve the Luus benchmark problem where the Pontryagin's minimum principle fails to produce a solution. This is ...
APMonitor.com
OptaPro Analytics Forum 2019 – Optimal coaching: how the tools of data science can help
Mathieu Rosenbaum and Othmane Mounjid present at the OptaPro Analytics Forum. Their presentation introduces artificial intelligence concepts, developed ...
Opta
6.047/6.878 Lecture 12 - Deep Learning (Fall 2020)
6.047/6.878/HST.507 Fall 2020 Prof. Manolis Kellis Computational Biology: Genomes, Networks, Evolution, Health Machine Learning in Genomics: Dissecting ...
Manolis Kellis
Optimal Lower Bounds for Distributed and Streaming Spanning Forest Computation
Huacheng Yu (Harvard University) https://simons.berkeley.edu/talks/optimal-lower-bounds-distributed-and-streaming-spanning-forest-computation Interactive ...
Simons Institute
Richard M. Karp Distinguished Lecture – Safe Learning in Robotics
Claire Tomlin (UC Berkeley) https://simons.berkeley.edu/events/rmklectures2020-fall-2 Richard M. Karp Distinguished Lecture.
Simons Institute
Nature-inspired metaheuristic algorithms for finding optimal designs
Weng Kee Wong University of California, Los Angeles, USA.
Institute for Mathematical Sciences
ROC and AUC in R
This tutorial walks you through, step-by-step, how to draw ROC curves and calculate AUC in R. We start with basic ROC graph, learn how to extract thresholds ...
StatQuest with Josh Starmer
How to Set Up Postman for Optimal JSON API Debugging
This video covers the basics of Postman, a Chrome app for testing APIs. See how to define sharable configurations and use environment variables to your ...
Soliant TV
Emilie Kaufmann - Optimal Best Arm Identification with Fixed Confidence
This talk proposes a complete characterization of the complexity of best-arm identification in one-parameter bandit models. We first give a new, tight lower bound ...
Institut des Hautes Études Scientifiques (IHÉS)
