Probabilistic modeling with TensorFlow Probability
Rethinking machine learning
Probabilistic models enable you to easily encode your or your company’s institutional knowledge into the model before you start collecting data, allowing you to make probabilistic inferences automatically from datasets that need not be large or even clean. Unlike many popular machine learning models, such as neural networks, probabilistic models are not black boxes. These models enable you to infer causes from effects in a fairly transparent manner. This is important in heavily regulated industries, such as finance and health care, where you have to explain the basis of your decisions. In addition, the conventional use of maximum likelihood estimates (MLE) in models can lead to costly assessments of risks. It’s imperative that all models quantify the uncertainty inherent in their point estimates so that sound business decisions can be made under uncertainty.
You can quantify the uncertainty in your estimates quite easily using TensorFlow Probability (TFP), one of the most powerful open source probabilistic machine learning libraries. TFP gives you the tools to build and fit complex probabilistic models using a few simple lines of Python code—letting you focus on model building and evaluation while automating the necessary statistical inferences.
In this handson fourhour course, Deepak Kanungo teach you to use TFP to quantify the uncertainty inherent in all point estimates. Join in to learn how to make realistic probabilistic predictions without making unrealistic assumptions in your models, enabling you to make sound business decisions in the face of uncertainty.
What you'll learnand how you can apply it
By the end of this live online course, you’ll understand:
 The sources of errors in models
 The hazards of using conventional statistics to quantify uncertainty in estimates
 The benefits of quantifying uncertainty using Bayesian inference
 How to explicitly encode personal and institutional knowledge into your models
 The advantages of using TFP to learn from small datasets
 The concepts behind Bayesian linear regression
 The underlying principles of change point test analysis of your business processes
 Stateoftheart algorithms like Markov chain Monte Carlo (MCMC), NoUTurn Sampler (NUTS), and automatic differential variational inference (ADVI) at a high level
And you’ll be able to:
 Build probabilistic models in TFP for your business processes
 Use these models to quantify the uncertainty in your company’s cost of capital so that you can make better capital budgeting decisions
 Use these models to estimate the uncertainty around change point tests in your business processes for quality control, intrusion detection, medical diagnostics, spam filtering, and website tracking
 Continually update your estimates based on new data
This training course is for you because...
 You’re an analyst or developer who needs to build probabilistic models that quantify the uncertainty in your estimates or forecasts.
Prerequisites
 A basic understanding of probability and statistics (Read “Seeing Theory” for a visual overview.)
 A working knowledge of Python programming
Recommended preparation:
 Set up a free Colaboratory account and create an empty Colab document
 Read “The Golem of Prague” and “Small Worlds and Large Worlds” (chapters 1 and 2 in Statistical Rethinking)
 Read “What Is Probabilistic Programming?” (article)
 Play the simulated Monty Hall game (For context, read “Understanding the Monty Hall Problem.”)
Recommended followup:
 Read Bayesian Methods For Hackers: Probabilistic Programming And Bayesian Inference (book)
 Watch Deep Dive into Probabilistic Machine Learning (video, 42m)
 Explore “Probabilistic Programming from Scratch 3: Performance and PyMC3” (O’Reilly oriole)
About your instructor

Deepak Kanungo is the founder and CEO of Hedged Capital LLC, an AIpowered trading and advisory firm. Previously, Deepak was a financial advisor at Morgan Stanley, a Silicon Valley fintech entrepreneur and a Director in the Global Planning Department at MasterCard International. Deepak was educated at Princeton University (Astrophysics) and The London School of Economics (Finance and Information Systems). Hedged Capital’s trading algorithms use probabilistic models and technologies such as TFP. In 2005, Deepak invented a project portfolio management system using Bayesian Inference, the foundation of all probabilistic programming languages.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction and the Monty Hall problem (15 minutes)
 Group discussion: Introduction; your experience with Python and statistics
 Handson exercises: Explore the Monty Hall problem through the online simulator
Epistemic probability (15 minutes)
 Group discussion: Epistemic probability; how it differs from the frequentist view of probability, on which much of conventional statistics is based
Bayesian inference (25 minutes)
 Lecture: The results from the game; why Bayesian inference offers a solution to the apparent paradox; Bayes’s theorem, the fundamental algorithm of all probabilistic programming languages
 Group discussion and Q&A
 Break (5 minutes)
Setup (5 minutes)
 Lecture: A quick validation of the environment; a brief review of the features of Colab notebook
TensorFlow Probability (TFP) (50 minutes)
 Lecture: The basic concepts and declarative commands in Python code used for building probabilistic models in TFP
 Handson exercises: Walk through the builtin change point test analysis model in the Colab notebook and analyze its output graphs
 Group discussion and Q&A
 Break (5 minutes)
Statistical analysis (15 minutes)
 Handson exercises: Run the builtin market model (MM) that uses standard linear regression with various start and end dates to draw 10 random samples to compute alpha, beta, and sample error of your company’s stock (or a proxy stock if private), including the 95% confidence intervals for all parameters; note your company’s cost of capital and other results in your notebook
Types of modeling errors (15 minutes)
 Group discussion: The sources of errors in models; the imperative need for quantifying uncertainty in your estimates
Confidence intervals (10 minutes)
 Lecture: The conventional meaning of probability; how confidence intervals are actually meant to be used
Quantifying uncertainty (15 minutes)
 Group discussion: Why it’s inappropriate to use confidence intervals to quantify uncertainty in estimates that are not normally distributed
 Break (5 minutes)
TFP algorithms (30 minutes)
 Lecture: The basic concepts behind the Markov chain Monte Carlo (MCMC), NoUTurn Sampler (NUTS), and automatic differential variational inference (ADVI) algorithms; what problems they’re best suited to address
Bayesian regression (20 minutes)
 Handson exercises: Recode the MM model in TFP using Bayesian linear regression; produce credible intervals for your company’s cost of capital and all other relevant parameters
Wrapup and Q&A (10 minutes)