MIT 6.0002 11. Introduction to Machine Learning
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MIT 6.0002 11. Introduction to Machine Learning

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016View the complete course: http://ocw.mit.edu/6-0002F16Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SAMore information at http://ocw.mit.edu/termsMore courses at http://ocw.mit.edu
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DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning

Unsupervised learning is one of the three major branches of machine learning (along with supervised learning and reinforcement learning). It is also arguably the least developed branch. Its goal is to find a parsimonious description of the input data by uncovering and exploiting its hidden structures. This is presumed to be more reminiscent of how […]
CoRL 2020, Spotlight Talk 465: Self-Supervised Learning of Scene-Graph Representations for Robotics
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CoRL 2020, Spotlight Talk 465: Self-Supervised Learning of Scene-Graph Representations for Robotics

**Self-Supervised Learning of Scene-Graph Representations for Robotic Sequential Manipulation Planning** Son Nguyen (University Stuttgart)*; Ozgur Oguz (Uni. of Stuttgart & Max Planck Inst. for Intelligent Systems ); Valentin Hartmann (University of Stuttgart); Marc Toussaint (Technische Universität Berlin) Publication: http://corlconf.github.io/paper_465/ **Abstract** We present a self-supervised representation learning approach for visual reasoning and integrate it into a […]
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Lecture 1 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent […]
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Will there be Human-Level Artificial Intelligence by 2030?

Artificial Intelligence has been improving rapidly these past few years, and it’s now becoming obvious according to the top AI Scientists such as Yann LeCun that AI in 2030 will be almost unrecognizable compared to ones right now. They will be part of everyday lifes in the form of digital assistants and more. What other […]
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Self Supervised Learning

What is self-supervised learning? How do you learn without labeling your data? William Falcon, PyTorch Lightning founder, and Ananya Harsh Ja, Lightning research engineer, deep dive into simCLR (“A Simple Framework for Contrastive Learning of Visual Representations”), self-supervised representation learning on images. Start here for the intro: https://www.youtube.com/watch?v=pDJx8i3jenA&list=PLaMu-SDt_RB4k8VXiB3hOdsn0Y3GoXo1k In the next videos, we will go […]
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Lecture 12 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen’s inequality, mixture of Gaussians, and expectation-maximization. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement […]
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Crash Course In Machine Learning Part 2 – What Is Supervised Learning

In part 2 of a crash course in machine learning, we get an overview of three big algorithms for supervised learning. Linear regression for calculating continuous variables Logistic regression and neural networks for calculating discrete outputs Learn how to turn deep reinforcement learning papers into code: Deep Q Learning: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-OCT-21 Actor Critic Methods: https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-OCT-21 Curiosity […]
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CoRL 2020, Spotlight Talk 324: Multi-Modal Anomaly Detection for Unstructured and Uncertain Envir…

“**Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments** Tianchen Ji (University of Illinois at Urbana-Champaign)*; Sri Theja Vuppala (University of Illinois at Urbana-Champaign); Girish Chowdhary (University of Illinois at Urbana Champaign); Katherine Driggs-Campbell (University of Illinois at Urbana-Champaign) Publication: http://corlconf.github.io/paper_324/ **Abstract** To achieve high-levels of autonomy, modern robots require the ability to detect and recover […]
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Lecture 2 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
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Studying the brain to build AI that processes language as people do

AI systems are still far from human-level intelligence, but with the rise of self-supervised learning (SSL) techniques, AI has gotten smarter, more contextually aware, and more adaptable. Researchers’ have done groundbreaking work on “cracking the neural code”—seeking to understand the human brain’s unique ability to learn and process language. Learn more on our blog about […]
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Supervised Learning with a Neural Network (C1W1L03)

Take the Deep Learning Specialization: http://bit.ly/2wWBgmn Check out all our courses: https://www.deeplearning.ai Subscribe to The Batch, our weekly newsletter: https://www.deeplearning.ai/thebatch Follow us: Twitter: https://twitter.com/deeplearningai_ Facebook: https://www.facebook.com/deeplearningHQ/ Linkedin: https://www.linkedin.com/company/deeplearningai Source of this AI Video
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Lecture 13 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include […]
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Trends in Computer Vision with Amir Zamir – #338

Today we close out AI Rewind 2019 joined by Amir Zamir, who recently began his tenure as an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology. Amir joined us back in 2018 to discuss his CVPR Best Paper winner, and in today’s conversation, we continue with the thread of Computer Vision. […]
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2021's Biggest Advancements in Artificial Intelligence

2021 has been an incredible year in terms of advancements in the field of Artificial Intelligence Technologies. AI has managed to gain new abilities and managed to achieve the futuristic feat of taking over several jobs which previously only humans could perform. Whether it’s self-supervised learning, custom AI accelerators or neuromorphic chips, the future of […]
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Deep Learning Basics: Introduction and Overview

An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, […]
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Lecture 3 | Machine Learning (Stanford)

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/BGwS/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. This course provides a broad introduction to machine learning and […]
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SwAV PyTorch Lightning Implementation

In this video we go over PyTorch Lightning implementation from scratch of “SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments”. SwAV is the latest paper from FAIR and Inria to post state of the art results in self-supervised learning. The paper combines ideas from contrastive learning and clustering based approaches to train image […]
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Lecture 14 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]
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Torchtext 0.4 with Supervised Learning Datasets: A Quick Introduction by George Zhang

Torchtext is a domain library for PyTorch that provides the fundamental components for working with text data, such as commonly used datasets and basic preprocessing pipelines, designed to accelerate natural language processing (NLP) research and machine learning (ML) development. George Zhang, a PyTorch Software Engineer, walks through the torchtext 0.4 release at the PyTorch Summer […]
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CoRL 2020, Spotlight Talk 464: Self-Supervised 3D Keypoint Learning for Ego-Motion Estimation

“**Self-Supervised 3D Keypoint Learning for Ego-Motion Estimation** Jiexiong Tang (KTH Royal Institute of Technology)*; Rareș Ambruș (Toyota Research Institute); Vitor Guizilini (Toyota Research Institute); Sudeep Pillai (Toyota Research Institute); Hanme Kim (Toyota Research Institute); Patric Jensfelt (Royal Institute of Technology); Adrien Gaidon (Toyota Research Institute) Publication: http://corlconf.github.io/paper_464/ **Abstract** Detecting and matching robust viewpoint-invariant keypoints is […]
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Ishan Misra: Self-Supervised Deep Learning in Computer Vision | Lex Fridman Podcast #206

Ishan Misra is a research scientist at FAIR working on self-supervised visual learning. Please support this podcast by checking out our sponsors: – Onnit: https://lexfridman.com/onnit to get up to 10% off – The Information: https://theinformation.com/lex to get 75% off first month – Grammarly: https://grammarly.com/lex to get 20% off premium – Athletic Greens: https://athleticgreens.com/lex and use […]
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Lecture 4 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton’s method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
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SwAV Loss Deep Dive

In this video, we dive into the loss function used in “SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments”. SwAV is the latest paper from FAIR and Inria to post state of the art results in self-supervised learning. The paper combines ideas from contrastive learning and clustering based approaches to train image representations. […]