A first course in machine learning
Material type:
- 9781498738484
- 006.31 R6F4
Item type | Current library | Item location | Collection | Shelving location | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|---|
Books | Vikram Sarabhai Library | Rack 4-A / Slot 107 (0 Floor, West Wing) | Non-fiction | General Stacks | 006.31 R6F4 (Browse shelf(Opens below)) | Available | 203316 |
Table of Contents
Linear Modelling: A Least Squares Approach.
Linear Modelling: A Maximum Likelihood Approach.
The Bayesian Approach to Machine Learning.
Bayesian Inference.
Classification.
Clustering.
Principal Components Analysis and Latent Variable Models.
Further Topics in Markov Chain Monte Carlo.
Classification and Regression with Gaussian Processes.
Dirichlet Process models.
Introduces the main algorithms and ideas that underpin machine learning techniques and applications -- Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations -- Covers modern machine learning research and techniques -- Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.
https://www.routledge.com/A-First-Course-in-Machine-Learning/Rogers-Girolami/p/book/9780367574642
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