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1.1-Machine Learning.pdf

12.1 MB
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1.2-Types of ML.pdf

2.54 MB
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1.3-Model Selection.pdf

927 kB

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10-Dual SVM and Kernels.pdf

2.37 MB
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11-Feature Maps and Kernels.pdf

2.3 MB
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12-SMO and Stochastic SVM.pdf

1.07 MB
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13-Regression.pdf

1.14 MB
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14-Principle Component Analysis.pdf

1.5 MB
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15-Clustering and Mixture Models.pdf

1.47 MB
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15.1-GMM Degeneracy.pdf

214 kB

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16-Variational EM and K-Means.pdf

2.38 MB
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17-Probabilistic Graphical Models and Bayesian Networks.pdf

1.04 MB
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18-Markov Models.pdf

4.5 MB
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19-Hidden Markov Models.pdf

2.17 MB
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2-Probability and Naive Bayes.pdf

409 kB

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20-Undirected Graphical Models.pdf

1.07 MB
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21-Belief Propagation.pdf

748 kB

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21.1-Graphical Models Wrap-up.pdf

994 kB

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22-What is Learning Theory_.pdf

132 kB

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23-Model Complexity and VC Dimension.pdf

521 kB

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3-Decision Trees.pdf

3.13 MB
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4-Logistic Regression.pdf

2.03 MB
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5-Perceptron.pdf

978 kB

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6-Neural Networks.pdf

2.6 MB
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7-Learning Objective Functions.pdf

927 kB

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8-Back Propagation.pdf

1.94 MB
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9-Support Vector Machines.pdf

1.04 MB
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CRC.Press-Utility.Based.Learning.from.Data.2010.RETAiL.EBook.pdf

3.41 MB
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Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann.pdf

8.14 MB
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ISLP_website.pdf

20.1 MB
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Introduction to Machine Learning - Nils J Nilsson.pdf

2.69 MB
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Machine Learning - Tom Mitchell.pdf

38.8 MB
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Machine Learning And Its Applications - Georgios Paliouras.pdf

19.2 MB
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Machine Learning in Computer Vision - N. SEBE.pdf

6.83 MB
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Machine Learning, Game Play, and Go - David Stoutamire.pdf

2.09 MB
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Machine Learning, Neural And Statistical Classification - Michie , Spiegelhalter , Taylor.pdf

21.1 MB
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PROBLEM SOLVING WITH REINFORCEMENT LEARNING - Gavin Adrian Rummery.pdf

1.13 MB
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Reinforcement Learning An Introduction - Richard S. Sutton , Andrew G. Barto.pdf

3.76 MB
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SQLNotesForProfessionals.pdf

1.59 MB
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Statistical Machine Learning For Information Retrieval - Adam Berger.pdf

1.46 MB
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