Efficient reasoning in PAC semantics - Brendan Juba
Brendan Juba Harvard University November 18, 2013 Machine learning is often employed as one step in a larger application, serving to perform information extraction or data mining for example. The rules obtained by such learning are then used as inputs to a further analysis. As a consequenc
From playlist Mathematics
From playlist Machine Learning
How to pick a machine learning model 3: Choosing a loss function
Part of the End-to-End Machine Learning School course library at http://e2eml.school See these concepts used in an End to End Machine Learning project: https://end-to-end-machine-learning.teachable.com/p/polynomial-regression-optimization/ Watch the rest of the How to Choose a Model serie
From playlist E2EML 171. How to Choose Model
A Friendly Introduction to Machine Learning
Grokking Machine Learning Book: https://www.manning.com/books/grokking-machine-learning 40% discount promo code: serranoyt A friendly introduction to the main algorithms of Machine Learning with examples. No previous knowledge required. What is Machine Learning: (0:05) Linear Regression:
From playlist General Machine Learning
Machine Learning by Andrew Ng [Coursera] 06-02 Machine learning system design
From playlist Machine Learning by Professor Andrew Ng
Adapt this pattern to solve many Machine Learning problems
Here's a simple pattern that can be adapted to solve many ML problems. It has plenty of shortcomings, but can work surprisingly well as-is! Shortcomings include: - Assumes all columns have proper data types - May include irrelevant or improper features - Does not handle text or date colum
From playlist scikit-learn tips
Foundations for Learning in the Age of Big Data I - Maria Florina Balcan
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Foundations for Learning in the Age of Big Data I Speaker: Maria Florina Balcan Affiliation: Carnegie Mellon University Date May 23, 2022 Balcan-2022-05-23
From playlist Mathematics
REFERENCES [1] A Short Introduction to Boosting: https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf [2] A Theory of the Learnable (Valiant, 1984): http://web.mit.edu/6.435/www/Valiant84.pdf. This introduced the PAC Learning model [3] PAC Learning Model: https://www.youtube.com/wa
From playlist Algorithms and Concepts
Reproducibility in Learning - Jessica Sorrell
Computer Science/Discrete Mathematics Seminar I Topic: Reproducibility in Learning Speaker: Jessica Sorrell Affiliation: University of California San Diego Date: January 24, 2022 Reproducibility is vital to ensuring scientific conclusions are reliable, but failures of reproducibility hav
From playlist Mathematics
Lecture 0610 Error metrics for skewed classes
Machine Learning by Andrew Ng [Coursera] 06-02 Machine learning system design
From playlist Machine Learning by Professor Andrew Ng
Cascadia Ruby 2014- Cognitive Shortcuts: Models, Visualizations, Metaphors, and Other Lies
By Sam Livingston-Gray Experienced developers tend to build up a library of creative problem-solving tools: rubber ducks, code smells, anthropomorphizing code, et cetera. These tools map abstract problems into forms our brains are good at solving. But our brains are also good at lying to
From playlist Cascadia Ruby 2014
Treatment of dementia and Alzheimer's disease | Mental health | NCLEX-RN | Khan Academy
Visit us (http://www.khanacademy.org/science/healthcare-and-medicine) for health and medicine content or (http://www.khanacademy.org/test-prep/mcat) for MCAT related content. These videos do not provide medical advice and are for informational purposes only. The videos are not intended to
From playlist Mental health | NCLEX-RN | Khan Academy
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 - Fast Reinforcement Learning III
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
What Are Error Intervals? GCSE Maths Revision
What are error Intervals and how do we find them - that's the mission in this episode of GCSE Maths minis! Error Intervals appear on both foundation and higher tier GCSE maths and IGCSE maths exam papers, so this is excellent revision for everyone! DOWNLOAD THE QUESTIONS HERE: https://d
From playlist Error Intervals & Bounds GCSE Maths Revision
Lecture 02 - Is Learning Feasible?
Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes
From playlist Machine Learning Course - CS 156
Mathematical Theories of Interaction with Oracles: Active Testing and Models - Liu Yang
Liu Yang School of Computer Science, Carnegie Mellon University February 11, 2013 With the notion of interaction with oracles as a unifying theme of much of my dissertation work, I discuss novel models and results for property testing and computational learning, with the use of Fourier ana
From playlist Mathematics
Learning from positive examples - Anindya De
Learning from positive examples - Anindya De Anindya De Institute for Advanced Study; Member, School of Mathematics November 5, 2013 We introduce and study a new type of learning problem for probability distributions over the Boolean hypercube {−1,1}n. As in the standard PAC learning mode
From playlist Mathematics
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
A Genome-Wide View of Transcription Fidelity
A Genome-Wide View of Transcription Fidelity. Filmed May 11, 2021 Abstract: Replication, transcription, and translation are the most fundamental molecular processes shared across the tree of life. While DNA mutation rates have been characterized for dozens of species, the rate at which RN
From playlist NCGAS: Genomics Research webinar series