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Introduction Case Study Probabilistic Programming Conclusion
Introduction to Model-Based MachineLearning
Daniel Emaasit1
1Ph.D. StudentDepartment of Civil and Environmental Engineering
University of Nevada Las VegasLas Vegas, NV USA
July 20 2016
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Introduction Case Study Probabilistic Programming Conclusion
Introduction
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Introduction Case Study Probabilistic Programming Conclusion
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Introduction Case Study Probabilistic Programming Conclusion
Current Challenges in Adopting Machine Learning
I Generally, current challenges in adopting ML:I Overwhelming number of traditional ML methods to learnI Deciding which algorithm to use or whyI Some custom problems may not fit with any existing
algorithm
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Introduction Case Study Probabilistic Programming Conclusion
What is Model-Based Machine Learning?
I A different viewpoint for machine learning proposed byBishop (2013)1, Winn et al. (2015)2
I Goal:I Provide a single development framework which supports the
creation of a wide range of bespoke models
I The core idea:I all assumptions about the problem domain are made
explicit in the form of a model
1Bishop, C. M. (2013). Model-Based Machine Learning. PhilosophicalTransactions of the Royal Society A, 371, pp 1–17
2Winn, J., Bishop, C. M., Diethe, T. (2015). Model-Based MachineLearning. Microsoft Research Cambridge. http://www.mbmlbook.com.
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What is a Model in MBML?
I A Model:I is a set of assumptions, expressed in mathematical/graphical
formI expresses all parameters, variables as random variablesI shows the dependency between variables
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Key Ideas of MBML?
I MBML is built upon 3 key ideasI the use of Probabilistic Graphical Models (PGM)I the adoption of Bayesian MLI the application of fast, approximate inference algorithms
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Key Idea 1: Probabilistic Graphical Models
I Combine probability theory with graphs (e.g Factor Graphs)
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Key Idea 2: Bayesian Machine Learning
I Everything follows from two simple rules of probabilitytheory
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Key Idea 3: Inference Algorithms
I the application of fast, deterministic inference algorithms bylocal message passing
I Variational BayesI Expectation Propagation
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Introduction Case Study Probabilistic Programming Conclusion
Stages of MBML
I 3 stages of MBMLI Build the model: Joint probability distribution of all the
relevant variables (e.g as a graph)I Incorporate the observed dataI Perform inference to learn parameters of the latent
variables
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Special cases of MBML
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Benefits of MBML
I Potential benefits of this approachI Provides a systematic process of creating ML solutionsI Allows for incorporation of prior knowledgeI Allows for handling uncertainity in a principled mannerI Does not suffer from overfittingI Custom solutions are built for specific problemsI Allows for quick building of several alternative modelsI Easy to compare those alternativesI It’s general purpose: No need to learn the 1000s of existing
ML algorithmsI Separates model from inference/training code
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Introduction Case Study Probabilistic Programming Conclusion
Case Study
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Introduction Case Study Probabilistic Programming Conclusion
TrueSkillTM by Microsoft
I Objective: Determine the true skill of millions of playerson Xbox Live
I Why: So that players of the same skill can be matched witheach other
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Stage 1: Build the Model
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Introduction Case Study Probabilistic Programming Conclusion
Stage 2: Incorporate Observed data
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Stage 3: Learn the parameters
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Convergence
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Introduction Case Study Probabilistic Programming Conclusion
Multiple players - Model
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Introduction Case Study Probabilistic Programming Conclusion
Team players - Model
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Introduction Case Study Probabilistic Programming Conclusion
Skill through time - Model
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Introduction Case Study Probabilistic Programming Conclusion
Probabilistic Programming
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Introduction Case Study Probabilistic Programming Conclusion
What is Probabilistic Programming?
I A software package that takes the model and thenautomatically generate inference routines (even source code!)to solve a wide variety of models
I Takes programming languages and adds support for:I random variablesI constraints on variablesI inference
I Examples of PP software packagesI Infer.Net (C#, C++)I Stan (R, python, C++)I BUGSI churchI PyMC (python)
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How Probabilistic Programming works
I How infer.NET works
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Conclusion
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Closing Remarks
I Objective of webinar:I Introduce the basics of model-based machine learning,I Introduce probabilistic programming.
I Moving forward: Now you can look into specialized topicslike:
I Fast Bayesian inference techniques,I Model building,
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Introduction Case Study Probabilistic Programming Conclusion
References
1. J. Winn, C. Bishop, and T. Diethe, Model-Based MachineLearning,Microsoft Research,2015.
2. C. M. Bishop, “Model-based machine learning” Phil Trans RSoc, A 371: 20120222, Jan. 2013
3. T. Minka, J. Winn, J. Guiver, and D. Knowles, Infer.NET,Microsoft Research Cambridge,2010.
4. Stan Development Team, “Stan Modeling Language UsersGuide and Reference Manual,” Version 2.9.0, 2016.
5. Stan Development Team, “RStan: the R interface to Stan,”Version 2.9.0”.
6. D. Emaasit, A. Paz, and J. Salzwedel (2016). “AModel-Based Machine Learning Approach for CapturingActivity-Based Mobility Patterns using Cellular Data”.IEEE ITSC 2016. Under Review.
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Introduction Case Study Probabilistic Programming Conclusion
Contact Me
I [email protected] github.com/EmaasitI www.danielemaasit.comI [@Emaasit](https://twitter.com/Emaasit)
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Two players - Model
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Two players - Code
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Introduction Case Study Probabilistic Programming Conclusion
Multiple players - Model
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Introduction Case Study Probabilistic Programming Conclusion
Multiple players - Code
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Introduction Case Study Probabilistic Programming Conclusion
Team players - Model
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Introduction Case Study Probabilistic Programming Conclusion
Team players - Code
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Introduction Case Study Probabilistic Programming Conclusion
Skill through time - Model
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Introduction Case Study Probabilistic Programming Conclusion
Skill through time - Code
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Introduction Case Study Probabilistic Programming Conclusion
y
w x
τ
N
M
y
N
dot
w x
µw αw µx αx
τ
ατ βτ
N N
G
N
M
Figure 1: PCA model as a Bayesian network and a directed factorgraph.
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Introduction Case Study Probabilistic Programming Conclusion
X
T
θ
αθ
Multi
Multi
Dir
φ
αφ
Dir
∀1 ≤ i ≤ nd
∀d ∈ D
∀t ∈ T
Figure 2: Latent Dirichlet allocation as directed factor graph.
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