His talk is an overview of the machine learning course I have just taught at Cambridge University (UK) during the Lent term (Jan to March) 2012. The course is an introduction to basic concepts in probabilistic machine learning, focussing on statistical methods for unsupervised and supervised ...
(oct 2011) - 16 Perfectoid Spaces and the Weight-Monodromy Conject 1:43:31 André JOYAL - 14 A crash course in topos theory the big picture 1:13:22 Hugo Duminil-Copin - La marche aléatoire auto-évitante 1:01:15 Alain Aspect - Le photon onde ou particule L’étrangeté quantique ...
The equivalent in a probabilistic setup is, of course, “Hello uncertain world.” You can think of the probabilistic program as a simulation or a data sampler. I’ll start with some parameters and use them to generate data. For instance, let’s have two strings that are completely unknown...
The great success of empirical and semi-empirical models soon led researchers to investigate alternative methods of modeling battery aging from experimental data. Saha et al.11were some of the first to use a machine learning (ML) algorithm as part of a framework to model battery capacity fade a...
In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming; b) Apply the Viterbi Algorithm for part-of-speech (POS) ta
PyMC3 port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers: Focused on using Bayesian statistics in cognitive modeling. ...
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems. ...
We describe this computation model, the structure of a course based on it, and draw some conclusions from the experiences with such a course. 展开 关键词: Functional logic languages lazy evaluation narrowing residuation integration of paradigms ...
Chapter X1: Bayesian methods in Machine Learning and Model Validation We explore how to resolve the overfitting problem plus popular ML methods. Also included are probablistic explainations of ridge regression and LASSO regression. Tim Saliman's winning solution to Kaggle's Don't Overfit problem ...
data structures and topics of current interest related to machine learning and the analysis of large data sets.' Richard M. Karp, University of California, Berkeley'The new edition is great. I'm especially excited that the authors have added sections on the normal distribution, learning theory ...