Events

Workshop Description: In the workshop, first the attendees will revisit the basic concepts of Python programming related to (1) writing and executing Python scripts to perform basic tasks, (2) entering and executing basic Python commands in a Jupyter Notebook, and (3) creating objects, data types such as strings, integers, Booleans, variables, lists, loops, coordinate system, if-statements, inequalities, etc. Later, this workshop will discuss the implementation of random variables and probability models in Python. In particular, we will introduce numpy that includes the basic understanding of arrays, matrices, matrices operations, random data generation and exercises. Furthermore, since understanding of Matplotlib is necessary to iplot functions and models in Python, we will explore basic strategies to plot using matplotlib Speaker(s): Taha Sajjad

Not sure how to find an internship? Unclear about how internships are structured? Join a Ryerson University Career & Co-op Centre, IEEE, and IEEE Women in Engineering collaboration for this informative workshop to learn about internship opportunities available for undergraduate and graduate students on Sept. 16 from 6-7 pm.

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