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Machine Learning: A Probabilistic Perspective

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With the ever increasing amounts of data in electronic form, the need for automated methods for data analysis continues to grow. The goal of machine learning is to develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest. Machine learning is thus closely related to the fields of statistics and data mining, but differs slightly in terms of its emphasis and terminology. This book provides a detailed introduction to the field, and includes worked examples drawn from application domains such as molecular biology, text processing, computer vision, and robotics

TÀI LIỆU LIÊN QUAN

Lecture Machine learning (2014-2015) - Lecture 10: Max-margin learning

Lecture Machine learning (2014-2015) - Lecture 12: Reinforcement learning

Machine Learning Introduction

Lecture Machine learning (2014-2015) - Lecture 1: Introduction

Lecture Machine learning (2014-2015) - Lecture 02: Linear prediction

Lecture Machine learning (2014-2015) - Lecture 3: Maximum likelihood

Lecture Machine learning (2014-2015) - Lecture 4: Nonlinear ridge regression risk, regularization, and cross-validation

Lecture Machine learning (2014-2015) - Lecture 5: Optimisation

Lecture Machine learning (2014-2015) - Lecture 6: Logistic regression: a simple ANN

Lecture Machine learning (2014-2015) - Lecture 7: Backpropagation: A modular approach (Torch NN)
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