here you goThings I’ve made, read and saved — shared here.

Archive

9,609 entries: 53 from X, 8,925 from Pinboard, saved 1998 to 2026.

Browse by tag, site, author, folder or language

Tags

Sites

Authors

Folders

Languages

Showing 1–50 of 912014 ×

2014

CMU 11-761 / Spring 2014 Language and Statistics

cs.cmu.edu

Internet search, speech recognition, machine translation, question answering, information retrieval, biological sequence analysis -- are all at the forefront of this century’s information revolution. In addition to their use of machine learning, these technologies rely heavily on classic statistical estimation techniques. Yet most CS and engineering undergraduate programs do not prepare students in this area beyond an introductory probability & statistics course. This course is designed to address this gap.

The goal of "Language and Statistics" is to ground the data-driven techniques used in language technologies in sound statistical methodology. We start by formulating various language technology problems in both an information theoretic framework (the source-channel paradigm) and a Bayesian framework (the Bayes classifier). We then discuss the statistical properties of words, sentences, documents and whole languages, and the various computational formalisms used to represent language. These discussions naturally lead to specific concepts in statistical estimation.

10601A14spring Machine learning

sites.google.com

Machine Learning (ML) asks "how can we design programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experience, e.g. spotting high-risk medical patients, recognizing speech, classifying text documents, detecting credit card fraud, or driving autonomous robots.

Topics covered in 10-601A include concept learning, version spaces, decision trees, neural networks, computational learning theory, active learning, estimation & the bias-variance tradeoff, hypothesis testing, Bayesian learning, the Minimum Description Length principle, the Gibbs classifier, Naïve Bayes classifier, Bayes Nets & Graphical Models, the EM algorithm, Hidden Markov Models, K-Nearest-Neighbors and nonparametric learning, reinforcement learning, genetic algorithms, bagging and boosting.