Supervised learning

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Related lectures (31)

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

Entity & Information Extraction

Explores information extraction using classifiers, features, and syntactic analysis.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Open-World Learning for Biomedical Data

Explores open-world learning for biomedical data, focusing on leveraging small datasets and discovering novel phenomena.

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Neural Networks: Supervised Learning and Backpropagation

Explains neural networks, supervised learning, and backpropagation for training and improving performance.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

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