A first course in machine learning. (2011)
- Record Type:
- Book
- Title:
- A first course in machine learning. (2011)
- Main Title:
- A first course in machine learning
- Further Information:
- Note: Simon Rogers, Mark Girolami.
- Other Names:
- Rogers, Simon, 1979-
Girolami, Mark, 1963- - Contents:
- Linear Modelling: A Least Squares Approach; Linear modelling; Making predictions; Vector/matrix notation; Nonlinear response from a linear model; Generalisation and over-fitting; Regularised least squares Linear Modelling: A Maximum Likelihood Approach; Errors as noise; Random variables and probability; Popular discrete distributions; Continuous random variables — density functions; Popular continuous density functions; Thinking generatively; Likelihood; The bias-variance tradeoff; Effect of noise on parameter estimates; Variability in predictions The Bayesian Approach to Machine Learning; A coin game; The exact posterior; The three scenarios; Marginal likelihoods; Hyper-parameters; Graphical models; A Bayesian treatment of the Olympics 100 m data; Marginal likelihood for polynomial model order selection; Summary Bayesian Inference; Nonconjugate models; Binary responses; A point estimate — the MAP solution; The Laplace approximation; Sampling techniques; Summary Classification; The general problem; Probabilistic classifiers; Nonprobabilistic classifiers; Assessing classification performance; Discriminative and generative classifiers; Summary Clustering; The general problem; K -means clustering; Mixture models; Summary Principal Components Analysis and Latent Variable Models; The general problem; Principal components analysis (PCA); Latent variable models; Variational Bayes; A probabilistic model for PCA; Missing values; Non-real-valued data; Summary Glossary Index ExercisesLinear Modelling: A Least Squares Approach; Linear modelling; Making predictions; Vector/matrix notation; Nonlinear response from a linear model; Generalisation and over-fitting; Regularised least squares Linear Modelling: A Maximum Likelihood Approach; Errors as noise; Random variables and probability; Popular discrete distributions; Continuous random variables — density functions; Popular continuous density functions; Thinking generatively; Likelihood; The bias-variance tradeoff; Effect of noise on parameter estimates; Variability in predictions The Bayesian Approach to Machine Learning; A coin game; The exact posterior; The three scenarios; Marginal likelihoods; Hyper-parameters; Graphical models; A Bayesian treatment of the Olympics 100 m data; Marginal likelihood for polynomial model order selection; Summary Bayesian Inference; Nonconjugate models; Binary responses; A point estimate — the MAP solution; The Laplace approximation; Sampling techniques; Summary Classification; The general problem; Probabilistic classifiers; Nonprobabilistic classifiers; Assessing classification performance; Discriminative and generative classifiers; Summary Clustering; The general problem; K -means clustering; Mixture models; Summary Principal Components Analysis and Latent Variable Models; The general problem; Principal components analysis (PCA); Latent variable models; Variational Bayes; A probabilistic model for PCA; Missing values; Non-real-valued data; Summary Glossary Index Exercises and Further Reading appear at the end of each chapter. … (more)
- Publisher Details:
- Place of publication not identified : Chapman and Hall/CRC
- Publication Date:
- 2011
- Extent:
- 1 online resource, illustrations
- Subjects:
- 006.31
Machine learning
BUSINESS & ECONOMICS / Statistics
COMPUTERS / General
COMPUTERS / Database Management / Data Mining - Languages:
- English
- ISBNs:
- 9781466506299
1466506296 - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.145483
- Ingest File:
- 02_150.xml