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

emaasit@unlv.nevada.edu

July 20 2016

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Introduction

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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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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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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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Case Study

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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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Stage 2: Incorporate Observed data

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Stage 3: Learn the parameters

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Convergence

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Multiple players - Model

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Team players - Model

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Skill through time - Model

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Probabilistic Programming

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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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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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Contact Me

I daniel.emaasit@gmail.comI 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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Multiple players - Model

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Multiple players - Code

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Team players - Model

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Team players - Code

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Skill through time - Model

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Skill through time - Code

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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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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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