Канал: Rishabh

Academia vs Industry: A Twelve Point Comparison

Academia vs Industry: A Twelve Point Comparison

Machine Learning Class: Neural Networks: Part II

Machine Learning Class: Neural Networks: Part II

Machine Learning Class: Neural Networks: Part I

Machine Learning Class: Neural Networks: Part I

Machine Learning Class: Gaussian Mixture Models and EM

Machine Learning Class: Gaussian Mixture Models and EM

Machine Learning Class: Ensembles, Bagging and Boosting: Part II

Machine Learning Class: Ensembles, Bagging and Boosting: Part II

Machine Learning Class: Ensembles, Bagging and Boosting: Part I

Machine Learning Class: Ensembles, Bagging and Boosting: Part I

Machine Learning Class: Bias Variance Tradeoff

Machine Learning Class: Bias Variance Tradeoff

Machine Learning Class: VC Dimension: Part II

Machine Learning Class: VC Dimension: Part II

Machine Learning Class: VC Dimension: Part I

Machine Learning Class: VC Dimension: Part I

Machine Learning Class: Computational Learning Theory: Part II

Machine Learning Class: Computational Learning Theory: Part II

Machine Learning Class: Computational Learning Theory: Part I

Machine Learning Class: Computational Learning Theory: Part I

Machine Learning Class: Clustering

Machine Learning Class: Clustering

Machine Learning Class: Logistic Regression

Machine Learning Class: Logistic Regression

Machine Learning Class: Naïve Bayes: Part II

Machine Learning Class: Naïve Bayes: Part II

Machine Learning Class: Naïve Bayes: Part I

Machine Learning Class: Naïve Bayes: Part I

Machine Learning Class: Bayesian Methods: Part II

Machine Learning Class: Bayesian Methods: Part II

Machine Learning Class: Bayesian Methods: Part I

Machine Learning Class: Bayesian Methods: Part I

Optimization in Machine Learning (Lecture 11): Applications

Optimization in Machine Learning (Lecture 11): Applications

Optimization in Machine Learning (Lecture 10): Difference of Submodular Opt, SCSC & SCSK

Optimization in Machine Learning (Lecture 10): Difference of Submodular Opt, SCSC & SCSK

Optimization in Machine Learning (Lecture 9):Submodular Maximization and Greedy

Optimization in Machine Learning (Lecture 9):Submodular Maximization and Greedy

Optimization in Machine Learning (Lecture 8): Polyhedra, Extensions, and Submodular Minimization

Optimization in Machine Learning (Lecture 8): Polyhedra, Extensions, and Submodular Minimization

Optimization in Machine Learning (Lecture 7 Continued): Submodular Information Measures

Optimization in Machine Learning (Lecture 7 Continued): Submodular Information Measures

Optimization in Machine Learning: Lec 7 (Submodular Functions: Definitions, Examples, Properties)

Optimization in Machine Learning: Lec 7 (Submodular Functions: Definitions, Examples, Properties)

Optimization in Machine Learning: Lecture 6.2 (SGD for Deep Learning)

Optimization in Machine Learning: Lecture 6.2 (SGD for Deep Learning)

Optimization in Machine Learning: Lecture 6.1 (Stochastic Gradient Descent)

Optimization in Machine Learning: Lecture 6.1 (Stochastic Gradient Descent)

Optimization in Machine Learning: Lecture 5 (Second Order Methods and Coordinate Descent)

Optimization in Machine Learning: Lecture 5 (Second Order Methods and Coordinate Descent)

Optimization in Machine Learning: Lec 4 (Conditional GD and Projected GD, Quasi-Newton, BFGS etc.)

Optimization in Machine Learning: Lec 4 (Conditional GD and Projected GD, Quasi-Newton, BFGS etc.)

Optimization in Machine Learning: Lec 3 (Gradient Descent Cont., Nesterov's GD, Proximal GD, Demos)

Optimization in Machine Learning: Lec 3 (Gradient Descent Cont., Nesterov's GD, Proximal GD, Demos)

Optimization in Machine Learning: Lecture 2 (Convex Functions Cont, Analysis of Gradient Descent)

Optimization in Machine Learning: Lecture 2 (Convex Functions Cont, Analysis of Gradient Descent)

Optimization in Machine Learning: Lecture 1 (Outline, Logistics, Convexity)

Optimization in Machine Learning: Lecture 1 (Outline, Logistics, Convexity)

IJCAI 2020 Tutorial Part II: Submodular Optimization for Data, Feature, and Topic Summarization.

IJCAI 2020 Tutorial Part II: Submodular Optimization for Data, Feature, and Topic Summarization.

IJCAI 2020 Tutorial Part I: Submodular Optimization for Data, Feature, and Topic Summarization.

IJCAI 2020 Tutorial Part I: Submodular Optimization for Data, Feature, and Topic Summarization.

Machine Learning Class: Nearest Neighbor Classifiers and k-d Trees

Machine Learning Class: Nearest Neighbor Classifiers and k-d Trees

Machine Learning Class: Decision Trees

Machine Learning Class: Decision Trees

Machine Learning Class: Support Vector Machines and Slack

Machine Learning Class: Support Vector Machines and Slack

Machine Learning Class: Lagrange Multipliers and Duality of SVMs (Part 2)

Machine Learning Class: Lagrange Multipliers and Duality of SVMs (Part 2)

Machine Learning Class: Lagrange Multipliers and Duality of SVMs (Part 1)

Machine Learning Class: Lagrange Multipliers and Duality of SVMs (Part 1)

Machine Learning Class: Intro to SVMs (Part 2) + Demo on SVMs

Machine Learning Class: Intro to SVMs (Part 2) + Demo on SVMs

Machine Learning Class: Intro to SVMs (Part 1)

Machine Learning Class: Intro to SVMs (Part 1)

Machine Learning Class: Perceptron (Part 4: Demo)

Machine Learning Class: Perceptron (Part 4: Demo)

Robust Submodular Minimization with Applications to Cooperative Modeling (ECAI 2020 Talk)

Robust Submodular Minimization with Applications to Cooperative Modeling (ECAI 2020 Talk)

Submodular Information Measures (SPCOM 2020 Invited Talk)

Submodular Information Measures (SPCOM 2020 Invited Talk)

Machine Learning Class: Perceptron (Part 3)

Machine Learning Class: Perceptron (Part 3)

Machine Learning Class: Perceptron (Part 2: Optimization and Sub-Gradient Descent)

Machine Learning Class: Perceptron (Part 2: Optimization and Sub-Gradient Descent)

Machine Learning Class: Perceptrons (Part 1)

Machine Learning Class: Perceptrons (Part 1)

Machine Learning Class: Regression (Part 4: Hands On)

Machine Learning Class: Regression (Part 4: Hands On)

Machine Learning Class: Regression (Part 3: Polynomial Regression)

Machine Learning Class: Regression (Part 3: Polynomial Regression)

Machine Learning Class: Regression (Part 2: Gradient Descent and Optimization)

Machine Learning Class: Regression (Part 2: Gradient Descent and Optimization)

Machine Learning Class: Regression (Part 1: Linear Regression Formulation)

Machine Learning Class: Regression (Part 1: Linear Regression Formulation)

Machine Learning Class: Introduction to ML (Part 4: Loss Functions and Evaluation)

Machine Learning Class: Introduction to ML (Part 4: Loss Functions and Evaluation)

Machine Learning Class: Introduction to ML (Part 3: Examples)

Machine Learning Class: Introduction to ML (Part 3: Examples)

Machine Learning Class: Introduction to ML (Part 2: Types of ML)

Machine Learning Class: Introduction to ML (Part 2: Types of ML)

Machine Learning Class: Introduction to ML (Part 1: Basics)

Machine Learning Class: Introduction to ML (Part 1: Basics)