Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 7.1 Polynomials and Step Functions
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: xhttps://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 12.1 Principal Components
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 2.4 Classification
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 2.1 Introduction to Regression Models
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
Statistical Learning: 3.3 Multiple Linear Regression
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 1.2 Examples and Framework
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 3.4 Some important questions
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Why The Best Data Scientists have Mastered Algebra, Calculus and Probability
All the outstanding data scientist and ML engineers have one thing in common: They have a strong, working understanding of how ML's high-level software libraries work. Being able to look under the hood, and understand what's going in libraries such as scikit-learn, TensorFlow, and Keras,
From playlist Talks and Tutorials
What Probability Theory Is — Topic 94 of Machine Learning Foundations
#MLFoundations #Probability #MachineLearning This video is a quick introduction to what Probability Theory is! There are eight subjects covered comprehensively in the ML Foundations series and this video is from the fifth subject, "Probability & Information Theory". More detail about th
From playlist Probability for Machine Learning
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
Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Surya Ganguli Describes how the application of methods from statistical physics to the analysis of high-dimensional data can provide theoretical insi
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
Math Major Guide | Warning: Nonstandard advice.
A guide for how to navigate the math major and how to learn the main subjects. Recommendations for courses and books. Comment below to tell me what you think. And check out my channel for conversation videos with guests on math and other topics: https://www.youtube.com/channel/UCYLOc-m8Wu
From playlist Math
This video will show you math subjects that most people never see. Many of these subjects are graduate level but some are also undergraduate level. What other areas of math do you think most people never see? Leave a comment below:) All the Math You Missed: https://amzn.to/3ZCaebJ Applied
From playlist Book Reviews
Mathematics of Machine Learning and the Ising Model (Clement Hongler) | Ep. 9
Clement Hongler is a professor of mathematics at EPFL in Switzerland. His research is in lattice models and conformal field theory, and also in neural networks. We discuss how he came to investigate these two different areas, and then talk about some problems in these fields. Clement's we
From playlist Daniel Rubin Show, Full episodes
Statistical Rethinking Fall 2017 - week01 lecture01
Week 01, lecture 01 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapters 1 and 2. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http
From playlist Statistical Rethinking Fall 2017
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
Deep Learning Lecture 2.4 - Statistical Estimator Theory
Deep Learning Lecture - Estimator Theory 3: - Statistical Estimator Theory - Bias, Variance and Noise - Results for Linear Least Square Regression
From playlist Deep Learning Lecture
Statistical mechanics of deep learning - Surya Ganguli
Workshop on Theory of Deep Learning: Where next? Topic: Statistical mechanics of deep learning Speaker: Surya Ganguli Affiliation: Stanford University Date: October 18, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics