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picture1_Basic Statistics Ppt 68587 | Lecture2


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File: Basic Statistics Ppt 68587 | Lecture2
probabilistic machine learning not all machine learning models are probabilistic but most of them have probabilistic interpretations predictions need to have associated confidence confidence probability arguments for probabilistic approach complete ...

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              Probabilistic Machine Learning
              • Not all machine learning models are probabilistic
                       • … but most of them have probabilistic interpretations
              • Predictions need to have associated confidence
                       • Confidence = probability
              • Arguments for probabilistic approach 
                       • Complete framework for Machine Learning
                       • Makes assumptions explicit
                       • Recovers most non-probabilistic models as special cases
                       • Modular: Easily extensible
                                 Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya                               2
              References
              • “Introduction to Probability Models”, Sheldon Ross
              • “Introduction to Probability and Statistics for Engineers and 
                  Scientists”, Sheldon Ross
              • “Introduction To Probability”, Dimitri P. Bertsekas, John N. 
                  Tsitsiklis
                                 Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya                               3
              Basics
              • Random experiment , outcome , events , sample space 
              •  
              • Probability measure 
              • Axioms of probability, basic laws of probability
              • Discrete sample space, discrete probability measure
              • Continuous sample space, continuous probability measure
              • Conditional probability, multiplicative rule, theorem of total probability, Bayes 
                  theorem
              • Independence, pair-wise, mutual, conditional independence
                                 Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya                               4
              Random Variables
              •  
              • Example:
                       • Experiment: Tossing of two coins
                       • Random variable: sum of two outcomes
                                 Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya                               5
              Discrete Random Variables
              • Probability mass function
                                 Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya                               6
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...Probabilistic machine learning not all models are but most of them have interpretations predictions need to associated confidence probability arguments for approach complete framework makes assumptions explicit recovers non as special cases modular easily extensible foundations algorithms and cs iit kgp indrajit bhattacharya references introduction sheldon ross statistics engineers scientists dimitri p bertsekas john n tsitsiklis basics random experiment outcome events sample space measure axioms basic laws discrete continuous conditional multiplicative rule theorem total bayes independence pair wise mutual variables example tossing two coins variable sum outcomes mass function...

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