Women in Engineering

We are living in the age of innovation. Every day, innovators are solving many problems that people are facing in life. In the process of innovation, there are many questions about how we can find problems. What is innovation exactly? How can we find solutions? And how can we learn the innovation process? I am Masoud Valinejad, CEO-Director of technology in NovoSolTech Company, and innovation mentor with more than five-year experience, with 10 USA patents, and more than five national and international special prizes in innovation competitions. In this webinar, I want to show you how you can become an innovator and entrepreneur through some steps and practices. Contact: Ayda Naserialiabadi

Product Lifecycle Management is a process used to manage all of the business and technical aspects in the life of a product business, from early stage concept to product retirement. It is used extensively by most Global MultiNational Corporations but it serves small startup businesses very well also. It deals with and includes participation from all of the important business organisations. As such it is very relevant to engineers involved in any aspect of product development. Marto Hoary has worked with a number of multinationals in the USA and Europe where in he has observed and learned the use of this process first hand. Speaker(s): Marto J Hoary, Sr MIEEE, M. Eng. Virtual: https://events.vtools.ieee.org/m/275555

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