A first course in machine learning:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
Chapman & Hall/CRC
2012
|
| Schriftenreihe: | Chapman & Hall, CRC machine learning & pattern recognition series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XX, 285 S. Ill., graph. Darst. |
| ISBN: | 9781439824146 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804143595116036096 |
|---|---|
| adam_text | Titel: A first course in machine learning
Autor: Rogers, Simon
Jahr: 2012
Contents
List of Tables xi
List of Figures xiii
Preface xix
1 Linear Modelling: A Least Squares Approach 1
1.1 Linear modelling......................... 1
1.1.1 Defining the model.................... 2
1.1.2 Modelling assumptions.................. 3
1.1.3 Defining what a good model is.............. 4
1.1.4 The least squares solution - a worked example .... 6
1.1.5 Worked example..................... 9
1.1.6 Least squares fit to the Olympics data......... 10
1.1.7 Summary......................... 11
1.2 Making predictions ....................... 12
1.2.1 A second Olympics dataset............... 12
1.2.2 Summary......................... 15
1.3 Vector/matrix notation ..................... 15
1.3.1 Example .......................... 22
1.3.2 Numerical example.................... 23
1.3.3 Making predictions.................... 24
1.3.4 Summary......................... 24
1.4 Nonlinear response from a linear model ............ 25
1.5 Generalisation and over-fitting ................. 28
1.5.1 Validation data...................... 29
1.5.2 Cross-validation ..................... 29
1.5.3 Computational scaling of K-iold cross-validation ... 32
1.6 Regularised least squares .................... 33
1.7 Exercises ............................. 35
Further reading ............................ 37
2 Linear Modelling: A Maximum Likelihood Approach 39
2.1 Errors as noise.......................... 39
2.1.1 Thinking generatively.................. 40
2.2 Random variables and probability ............... 41
viii Contents
5.2.1.4 Making predictions .............. 173
5.2.1.5 The naive Bayes assumption......... 175
5.2.1.6 Example - classifying text .......... 175
5.2.1.7 Smoothing................... 177
5.2.2 Logistic regression.................... 179
5.2.2.1 Motivation................... 180
5.2.2.2 Nonlinear decision functions......... 181
5.2.2.3 Nonparametric models - the Gaussian process 182
5.3 Nonprobabilistic classifiers ................... 183
5.3.1 X-nearest neighbours.................. 183
5.3.1.1 Choosing K .................. 184
5.3.2 Support vector machines and other kernel methods . . 186
5.3.2.1 The margin................... 186
5.3.2.2 Maximising the margin............ 187
5.3.2.3 Making predictions .............. 190
5.3.2.4 Support vectors................ 191
5.3.2.5 Soft margins.................. 192
5.3.2.6 Kernels..................... 193
5.3.3 Summary......................... 197
5.4 Assessing classification performance .............. 198
5.4.1 Accuracy - 0/1 loss ................... 198
5.4.2 Sensitivity and specificity................ 198
5.4.3 The area under the ROC curve............. 199
5.4.4 Confusion matrices.................... 201
5.5 Discriminative and generative classifiers............ 203
5.6 Summary............................. 203
5.7 Exercises ............................. 203
Further reading ............................ 205
6 Clustering 207
6.1 The general problem....................... 207
6.2 if-means clustering ....................... 208
6.2.1 Choosing the number of clusters............ 210
6.2.2 Where tf-means fails................... 212
6.2.3 Kernelised if-means................... 212
6.2.4 Summary......................... 214
6.3 Mixture models ......................... 215
6.3.1 A generative process................... 216
6.3.2 Mixture model likelihood ................ 217
6.3.3 The EM algorithm.................... 219
6.3.3.1 Updating 7Tfc.................. 220
6.3.3.2 Updating/xfc.................. 221
6.3.3.3 Updating Sfe.................. 222
6.3.3.4 Updating qnk.................. 223
6.3.3.5 Some intuition................. 224
Contents ix
6.3.4 Example.......................... 225
6.3.5 EM finds local optima.................. 226
6.3.6 Choosing the number of components.......... 228
6.3.7 Other forms of mixture components.......... 230
6.3.8 MAP estimates with EM ................ 232
6.3.9 Bayesian mixture models ................ 233
6.4 Summary............................. 234
6.5 Exercises ............................. 234
Further reading ............................ 237
7 Principal Components Analysis and Latent Variable Models 239
7.1 The general problem....................... 239
7.1.1 Variance as a proxy for interest............. 239
7.2 Principal components analysis ................. 242
7.2.1 Choosing D........................ 247
7.2.2 Limitations of PCA ................... 247
7.3 Latent variable models ..................... 248
7.3.1 Mixture models as latent variable models....... 248
7.3.2 Summary......................... 249
7.4 Variational Bayes ........................ 249
7.4.1 Choosing Q(0)...................... 251
7.4.2 Optimising the bound.................. 252
7.5 A probabilistic model for PCA................. 252
7.5.1 Qt{t)........................... 254
7.5.2 £x (xn).......................... 256
7.5.3 QWm(wm)......................... 257
7.5.4 The required expectations................ 258
7.5.5 The algorithm ...................... 258
7.5.6 An example........................ 260
7.6 Missing values .......................... 260
7.6.1 Missing values as latent variables............ 262
7.6.2 Predicting missing values................ 264
7.7 Non-real-valued data ...................... 264
7.7.1 Probit PPCA....................... 264
7.7.2 Visualising parliamentary data............. 268
7.7.2.1 Aside - relationship to classification..... 272
7.8 Summary............................. 273
7.9 Exercises ............................. 273
Further reading ........................¦••••. 275
Glossary 277
Index 283
|
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| spelling | Rogers, Simon 1979- Verfasser (DE-588)1129383253 aut A first course in machine learning Simon Rogers ; Mark Girolami Boca Raton, Fla. [u.a.] Chapman & Hall/CRC 2012 XX, 285 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall, CRC machine learning & pattern recognition series Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Machine learning. 1\p (DE-588)4151278-9 Einführung gnd-content Maschinelles Lernen (DE-588)4193754-5 s DE-604 Girolami, Mark 1963- Verfasser (DE-588)12096595X aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020800138&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
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| title | A first course in machine learning |
| title_auth | A first course in machine learning |
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| title_full | A first course in machine learning Simon Rogers ; Mark Girolami |
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| title_full_unstemmed | A first course in machine learning Simon Rogers ; Mark Girolami |
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