Probabilistic latent semantic analysis

Lecture 26 — Probabilistic Latent Semantic Analysis PLSA - Part 1 | UIUC

From playlist Text Mining and Analytics [FULL COURSE] | UIUC

Probabilistic logic programming and its applications - Luc De Raedt, Leuven

Probabilistic programs combine the power of programming languages with that of probabilistic graphical models. There has been a lot of progress in this paradigm over the past twenty years. This talk will introduce probabilistic logic programming languages, which are based on Sato's distrib

From playlist Logic and learning workshop

Lecture 50 — Contextual Text Mining Contextual Probabilistic Latent Semantic Analysis | UIUC

From playlist Text Mining and Analytics [FULL COURSE] | UIUC

An Overview of Predicate Logic for Linguists - Semantics in Linguistics

This video covers predicate logic in #semantics for #linguistics. We talk about predicates, quantifiers (for all, for some), how to translate sentences into predicate logic, scope, bound variables, free variables, and assignment functions. Join this channel to get access to perks: https:/

From playlist Semantics in Linguistics

R & Python - Latent Semantic Analysis

Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class - this video set covers the updated version with both R and Python. This video is the start of a section on vector space models. First up is latent

From playlist Human Language (ANLY 540)

Probabilistic model 5: summary of assumptions

[http://bit.ly/BM-25] The summary of 7 assumptions made in the probabilistic model of IR, and why really need to make them. What assumptions can we relax?

From playlist Probabilistic Model of IR

Computational Linguistics I: Topic Modeling

From playlist Digging into Data

Machine Learning @ Amazon by Rajeev Rastogi

DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr

From playlist The Theoretical Basis of Machine Learning 2018 (ML)

Suqi Liu (Princeton) -- A probabilistic view of latent space graphs and phase transitions

In this talk, I will present a probabilistic view of random graphs with latent geometric structure. By choosing a natural variance parameter, we show phase transitions of losing geometry in these graphs. The proofs make use of information-theoretic inequalities and concentration of measure

From playlist Northeastern Probability Seminar 2021

Truth Conditional Meaning in Model Theory (Fragment F1) - Semantics in Linguistics

We introduce the model theory of fragment F1 in Chierchia and McConnel-Ginet (2000)'s book on #semantics in #linguistics. We cover the meaning of proper nouns, intransitive verbs, transitive verbs, negation, and conjunctions, as well as how to derive meaning of larger constituents. We do a

From playlist Semantics in Linguistics

Lecture 19: Generative Models I

Lecture 19 is the first of two lectures about generative models. We compare supervised and unsupervised learning, and also compare discriminative vs generative models. We discuss autoregressive generative models that explicitly model densities, including PixelRNN and PixelCNN. We discuss a

From playlist Tango

Applied ML 2020 - 16 - Topic models for text data

From playlist Applied Machine Learning 2020

André Freitas - Building explanation machines for science: a neuro-symbolic perspective

Recorded 12 January 2023. André Freitas of the University of Manchester presents "Building explanation machines for science: a neuro-symbolic perspective" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/wor

From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights

R - Topics Models

Lecturer: Dr. Erin M. Buchanan Harrisburg University of Science and Technology Summer 2019 This video covers Topics Models and their comparison to other semantic vector space models, such as Latent Semantic Analysis. I cover what is a topic model, how to build one in R, and how to explor

From playlist Human Language (ANLY 540)

Ralf Herbrich: "Learning Real-World Probabilistic Models with Approximate Message Passing"

The Turing Lectures: Industrial & Commercial - Ralf Herbrich – Amazon: Learning Real-World Probabilistic Models with Approximate Message Passing Click the below timestamps to navigate the video. 00:00:10 Introduction by Professor Chris Williams, Edinburgh University 00:01:19 Ralf Herbric

From playlist Turing Lectures

Probabilistic model 3: parameter estimation

[http://bit.ly/BM-25] How do we estimate the parameters for the probabilistic model of IR? When we have examples of relevant and non-relevant documents (relevance feedback) the estimation is very straightforward: we use maximum-likelihood estimates for Bernoulli random variables (relative

From playlist Probabilistic Model of IR

Lecture 19 — Probabilistic Topic Models Mining One Topic | UIUC

From playlist Text Mining and Analytics [FULL COURSE] | UIUC

R & Python - Sentiment Analysis Part 2

Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my Natural Language Processing course. This video expands the previous classification videos into sentiment analysis. You will learn both unsupervised lexicon approaches along with

From playlist Natural Language Processing