Machine learning describes computer systems that are able to automatically perform tasks based on data. A machine learning system takes data as input and produces an approach or solution to a task as output, without the need for human intervention. Machine learning is closely tied to th
From playlist Data Science Dictionary
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
In this video, you’ll learn more about the evolution of machine learning and its impact on daily life. Visit https://www.gcflearnfree.org/thenow/what-is-machine-learning/1/ for our text-based lesson. This video includes information on: • How machine learning works • How machine learning i
From playlist Machine Learning
What is Quantum Machine Learning?
Generative machine learning is the field of ML that focuses on generating data. If you've seen any of the realistic-looking faces on pages such as www.thispersondoesnotexist.com or www.whichfaceisreal.com, you've seen generative machine learning in action. Quantum computing is a rapidly ad
From playlist Fundamentals of Machine Learning
What Is Supervised Learning In Machine Learning? | Machine Learning For Beginners | Simplilearn
This video on What is Supervised Learning in machine learning will take you through a detailed concept of Supervised Learning. This video will help you to understand What is Machine Learning, what is supervised learning, how supervised learning works, the advantages and disadvantages of su
Introduction To Machine Learning | Machine Learning Basics for Beginners | ML Basics | Simplilearn
Machine Learning is a trending topic nowadays. This Introduction to Machine Learning video will help you to understand what is Machine Learning, importance of Machine Learning, advantages and disadvantages of Machine Learning, what are the types of Machine Learning - supervised, unsupervis
This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels. Book websit
From playlist Intro to Data Science
Building efficient learning algorithms: a computational regularization perspective - Lorenzo Rosasco
The workshop aims at bringing together researchers working on the theoretical foundations of learning, with an emphasis on methods at the intersection of statistics, probability and optimization. Classical algorithms design in machine learning is based on minimizing an empirical objectiv
From playlist The Interplay between Statistics and Optimization in Learning
What Is An Algorithm ? | Introduction to Algorithms | How To Write An Algorithm? | Simplilearn
This video is based on What Is An Algorithm ? The Introduction to Algorithms tutorial will explain to you How To Write An Algorithm? and it will cover the following topics ✅00:00- Introduction to Algorithms ✅01:46- What Is an Algorithm? The algorithm is a step-by-step procedure or set o
From playlist C++ Tutorial Videos
David McAllester - Dependent Type Theory from the Perspective of Mathematics, Physics, and (...)
Dependent type theory imposes a type system on Zemelo-Fraenkel set theory (ZFC). From a mathematics and physics perspective dependent type theory naturally generalizes the Bourbaki notion of structure and provides a universal notion of isomorphism and symmetry. This comes with a universal
From playlist Mikefest: A conference in honor of Michael Douglas' 60th birthday
Lecture 01 - The Learning Problem
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunesu.itunes
From playlist Courses and Series
Benjamin Guedj: On generalisation and learning
A (condensed) primer on PAC-Bayes, followed by News from the PAC-Bayes frontline. LMS Computer Science Colloquium 2021
From playlist LMS Computer Science Colloquium Nov 2021
Unit 3 Debate: Tomer Ullman and Laura Schulz
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Tomer Ullman, Laura Schulz Speakers debate what makes a good theory of the world, the potential role of stochastic search in theory formation, goal-o
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
Tightening information-theoretic generalization bounds with data-dependent estimate... - Daniel Roy
Workshop on Theory of Deep Learning: Where next? Topic: Tightening information-theoretic generalization bounds with data-dependent estimates with an application to SGLD Speaker: Daniel Roy Affiliation: University of Toronto Date: October 15, 2019 For more video please visit http://video
From playlist Mathematics
Lecture 01 - The Learning Problem
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 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.apple.c
From playlist Machine Learning Course - CS 156
Statistical mechanics of deep learning by Surya Ganguli
Statistical Physics Methods in Machine Learning DATE: 26 December 2017 to 30 December 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru The theme of this Discussion Meeting is the analysis of distributed/networked algorithms in machine learning and theoretical computer science in the
From playlist Statistical Physics Methods in Machine Learning
Some success stories in bridging theory and practice in ML (Lecture 1) by Anima Anandkumar
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)
A Blueprint of Standardized and Composable Machine Learning - Eric Xing
Seminar on Theoretical Machine Learning Topic: A Blueprint of Standardized and Composable Machine Learning Speaker: Eric Xing Affiliation: Carnegie Mellon University Date: August 6, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Multi Type Mean Field Reinforcement Learning | AISC
For slides and more information on the paper, visit https://ai.science/e/multi-type-mean-field-reinforcement-learning--ZPQxNPfeGM02aiyTqViE Discussion lead: Sriram Ganapathi Subramanian, Matthew Taylor This paper presents scaling up RL to hundreds or thousands of agents using a "mean fie
From playlist Reinforcement Learning
Everything you need to know about Machine Learning!
Here is an introduction to Machine Learning. Instead of developing algorithms for every task and subtask to solve a problem, Machine Learning involves teaching a computer to teach itself. There are different types of machine learning problems we may come across. TYPES OF MACHINE LEARNING
From playlist Algorithms and Concepts