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Topics in machine learning

Related lectures (29)

Linear Binary Classification: Perceptron, SGD, Fisher's LDA

Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

SVM - Principle: Linear Classifiers

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Support Vector Machines

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Perceptron: Part 2

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Linear Models for Classification

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Support Vector Machines: SVMs

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Multiclass Classification

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduction to Learning by Stochastic Gradient Descent: Simple Perceptron

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Linear Models for Classification

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Related lectures (29)

Linear Binary Classification: Perceptron, SGD, Fisher's LDA

Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

SVM - Principle: Linear Classifiers

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Support Vector Machines

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Perceptron: Part 2

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Linear Models for Classification

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Support Vector Machines: SVMs

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Multiclass Classification

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduction to Learning by Stochastic Gradient Descent: Simple Perceptron

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Linear Models for Classification

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.