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Pattern Recognition and Machine Learning notes

Christopher M. Bishop - ISBN: 9781493938438

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View all 6 notes for Pattern Recognition and Machine Learning, written by Christopher M. Bishop. All Pattern Recognition and Machine Learning notes, flashcards, summaries and study guides are written by your fellow students or tutors. Get yourself a Pattern Recognition and Machine Learning summary or other study material that matches your study style perfectly, and studying will be a breeze.

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Introduction; Timeline; Man v/s Computer; Soft v/s Hard Classification
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This document contains class notes and lucid description of the following topics: 1. Introductory concepts of Artificial Intelligence 2. Why Machine Learning? 3. Timeline of Artificial Intelligence 4. Soft v/s Hard Classification 5. Various Machine Learning domains 6. Human brain v/s Computer

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Support Vector Machines
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Basic Summary of how Support Vector Machines Work, with historical background and the algorithms idea from the basic to Kernel functions.

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Pseudo Random Numbers
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This series of handwritten notes contains everything in a gist that a Computer Science or Statistics graduate student needs to study for his/her Machine Learning course. Topics covered: 1. History of Artificial Intelligence 2. The Turing Test 3. Weak AI v/s Strong AI 4. Human brain v/s Computer 5. Various Machine Learning domains 6. Feature Extraction 7. Soft Classification and Hard Classification 8. Linear Classifier 9. Evaluation Metrics 10. Probability Density Function 11. Probability Mass F...

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This document contains class notes and lucid description of the following topics: 1. Classification problems 2. Gradient Descent Algorithm 3. Data Normalization 4. Multi-class classification (including non-linearity and loss function)

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Feature Extraction; Dealing with data; Regression
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This document contains class notes and lucid description of the following topics: 1. Feature extraction 2. Dealing with data 3. Least square solution 4. Minimum norm solution 5. Exploring the IRIS dataset using Python 6. Regression

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Evaluation Metrics; Probability Functions; Tensors
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This document contains class notes and lucid description of the following topics: 1. Evaluation Metrics - Accuracy, Precision, Recall, F1 Score, PRC curve 2. Probability Density Function 3. Probability Mas Function 4. Cumulative Distribution Function 5. Dealing with tensors

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Introduction; Timeline; Man v/s Computer; Soft v/s Hard Classification
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This document contains class notes and lucid description of the following topics: 1. Introductory concepts of Artificial Intelligence 2. Why Machine Learning? 3. Timeline of Artificial Intelligence 4. Soft v/s Hard Classification 5. Various Machine Learning domains 6. Human brain v/s Computer

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