Women in Engineering

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

Prerequisites: If you know zero math and zero machine learning, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning, then these talks are for you. Jeff tries to spin the ideas in new ways. Abstract: Computers can now drive cars and find cancer in x-rays. For better or worse, this will change the world (and the job market). Strangely designing these algorithms is not done by telling the computer what to do or even by understanding what the computer does. The computers learn themselves from lots and lots of data and lots of trial and error. This learning process is more analogous to how brains evolved over billions of years of learning. The machine itself is a neural network which models both the brain and silicon and-or-not circuits, both of which are great for computing. The only difference with neural networks is that what they compute is determined by weights and small changes in these weights give you small changes in the result of the computation. The process for finding an optimal setting of these weights is analogous to finding the bottom of a valley. "Gradient Decent" achieves this by using the local slope of the hill (derivatives) to direct the travel down the hill, i.e. small changes to the weights. Speaker(s): Prof. Jeff Edmonds, Virtual: https://events.vtools.ieee.org/m/287252

This series of 5 beginner friendly workshops will teach students how to create their own indie game in Unity. We will teach the building blocks and best practices to create a shooter including creating the player, creating enemies, collectibles, effects, and more! All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a showcase with small prizes at the end of the workshop series. Week One: (2 Hours) - The Basics & Tile System - Introduction to Game Development & Unity (30 Minutes) ● Review of programming (30 minutes) ○ Variables ○ If statements ○ Loops ○ Classes and methods ○ Unity’s approach to programming - Break (10 minutes) - Quick demo of final game project (10 minutes) ● Download & import assets (10 minutes) - Introduction to the tile palette system (10 minutes) ● Draw game background using tile palette system (20 minutes) Virtual: https://events.vtools.ieee.org/m/287738

--- Prerequisites --- You do not need to have attended the earlier talks. If you know zero math and zero machine learning, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning, then these talks are for you. Jeff tries to spin the ideas in new ways. --- Longer Abstract --- An input data item, eg a image of a cat, is just a large tuple of real values. As such it can be thought as a point in some high dimensional vector space. Whether the image is of a cat or a dog partitions this vector space into regions. Classifying your image amounts to knowing which region the corresponding point is in. The dot product of two vectors tell us: whether our data scaled by coefficients meets a threshold; how much two lists of properties correlate; the cosine of the angle between to directions; and which side of a hyperplane your points is on. A novice reading a machine learning paper might not get that many of the symbols are not real numbers but are matrices. Hence the product of two such symbols is matrix multiplication. Computing the output of your current neural network on each of your training data items amounts to an alternation of such a matrix multiplications and of some non-linear rounding of your numbers to be closer to being 0-1 valued. Similarly, back propagation computes the direction of steepest decent using a similar alternation, except backwards. The matrix way of thinking about a neural network also helps us understand how a neural network effectively performs a sequence linear and non-linear transformations changing the representation of our input until the representation is one for which the answer can be determined based which side of a hyperplane your point is on. Though people say that it is "obvious", it was never clear to me which direction to head to get the steepest decent. Slides Covered: http://www.eecs.yorku.ca/~jeff/courses/machine-learning /Machine_Learning_Made_Easy.pptx - Linear Regression, Linear Separator - Neural Networks - Abstract Representations - Matrix Multiplication - Example - Vectors - Back Propagation - Sigmoid Speaker(s): Prof. Jeff Edmonds, Virtual: https://events.vtools.ieee.org/m/287446