Instrumentation & Measurement

In this workshop, we first provide a brief review of probability theory making sure that attendees understand probability models and applications. Later in this workshop, we will discuss basic probability models and their implementation in python, how to deal with various aspects of conditional probability like total probability theorem, conditional independence, Bayes Rule, etc. Then, we will discuss the implementation of discrete random variables as well as continuous random variable like Bernoulli variables, geometric variables, uniform, exponential and gaussian distribution. Afterwards, fundamental law of large numbers related programming concepts will be covered along with sample mean and variance of famous probability distributions. Speaker(s): Taha Sajjad, Virtual: https://events.vtools.ieee.org/m/277449

This workshop will cover an example project on Bayes Classifier, multiple random variables, and estimation. We will learn the implementation of multivariate Gaussian distribution, classification and regression problems in Python. Later we will see that how to define parametric distribution in python and will further explore estimation concepts like maximum likelihood ratio, maximum posteriori classification, loglikelihood and logistic regression. Speaker(s): Taha Sajjad, Virtual: https://events.vtools.ieee.org/m/277453