In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions. The goal is that, with high probability (the "probably" part), the selected function will have low generalization error (the "approximately correct" part). The learner must be able to learn the concept given any arbitrary approximation ratio, probability of success, or distribution of the samples. The model was later extended to treat noise (misclassified samples). An important innovation of the PAC framework is the introduction of computational complexity theory concepts to machine learning. In particular, the learner is expected to find efficient functions (time and space requirements bounded to a polynomial of the example size), and the learner itself must implement an efficient procedure (requiring an example count bounded to a polynomial of the concept size, modified by the approximation and likelihood bounds). (Wikipedia).
How to set up a lesson and add a content page
Shows you the options you need to select to set up a formative lesson with a content page.
From playlist How to create a lesson in your course
How to set a passing grade for a lesson
This video will show you how to only open up the rest of your course once the student gets a passing grade from one lesson.
From playlist How to create a lesson in your course
From playlist Machine Learning
In this video, you’ll learn more about the different types of learning styles, to see which one works best for you! Visit https://www.gcflearnfree.org/ to learn even more. We hope you enjoy!
From playlist Fundamentals of Learning
How to find the vector with given magnitude and same direction of another vector
http://www.freemathvideos.com In this video series you will learn multiple math operations. I teach in front of a live classroom showing my students how to solve math problems step by step. My math tutorials should be used to review previous lessons, complete your homework, or study for
From playlist Vectors
Learn Algebra 6 : Rate of Change
New Video Everyday at 1 PM EST!!! [ Click Notification Bell ] In this video I focus on Rate of Change, the Intercept Method and the Point to Point Method with numerous examples. I was asked by a local teacher to create an Algebra course that quickly reviewed all the key Algebra knowledge
From playlist Learn Algebra
New Video Everyday at 1 PM EST!!! [ Click Notification Bell ] I was asked by a local teacher to create an Algebra course that quickly reviewed all the key knowledge required. This course is centered around showing how to solve Algebra problems. For best results copy down the problem, watc
From playlist Machine Learning & Data Science
If Your Parents Didn’t Listen to You Properly...
There are few more important tasks for parents than to be able to listen properly to their children. It’s on the basis of having been listened to with close sympathy and imagination that a child will later on be able to accept themselves. Sign up to our new newsletter and get 10% off your
From playlist SELF
The intuitive idea of a function
Learning Objectives: Express the idea of a function as an "instruction", a "graph" and a "machine" that take inputs and spit out outputs. However there are constraints: every input must have a corresponding output, and more specifically just ONE corresponding output. ********************
From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)
Nexus Trimester - Ronitt Rubinfeld (MIT and Tel Aviv University) 2/2
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From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
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From playlist Learning resources
Fairness and robustness in machine learning – a formal methods perspective - Aditya Nori, Microsoft
With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate fairness and bias in decision-making programs. First, we show that a number of recently proposed formal definitions of fairness can be encoded as proba
From playlist Logic and learning workshop
Zuowei Shen: "Deep Learning: Approximation of functions by composition"
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From playlist New Deep Learning Techniques 2018
From playlist CS294-112 Deep Reinforcement Learning Sp17
Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
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From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
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From playlist Week 0 (Spring 2015)