Women in Engineering

Tuesday July 23rd, 2019 at 2:30 p.m. Reza Dibaj, Chair of Magnetics Chapter in the IEEE Toronto Section, will be presenting “IEEE Toronto Centennial Workshop: Distributed Machine Learning, Basic Concepts”. Day & Time: Tuesday July 23rd, 2019 2:30 p.m. ‐ 3:30 p.m. Speaker: Reza Dibaj Chair of Magnetics Chapter, IEEE Toronto Section Organizers: Magnetics Chapter, IEEE Toronto Centennial College Chapter, WIE IEEE Toronto Location: Room B3-09 Centennial College, Progress Campus 941 Progress Ave., Toronto, Ontario, M1G 3T8 Contact: Reza Dibaj Abstract: Machine Learning is an indispensable part of data science and there is no need to have a thorough programming background to benefit from it. Machine Learning (ML) and statistical techniques have provided a new era that enables us to convert the data to information, and transform the information into actionable knowledge. SciKit and TensorFlow are two states of the art libraries that can be used in Python and this seminar will open the gate to know their bases. The first seminar is about “Hello World!” Machine Learning program, using python language and SciKit learn library.

Tuesday July 30th, 2019 at 2:30 p.m. Reza Dibaj, Chair of Magnetics Chapter in the IEEE Toronto Section, will be presenting “IEEE Toronto Centennial Workshop: Distributed Machine Learning, The Second Step”. Day & Time: Tuesday July 30th, 2019 2:30 p.m. ‐ 3:30 p.m. Speaker: Reza Dibaj Chair of Magnetics Chapter, IEEE Toronto Section Organizers: Magnetics Chapter, IEEE Toronto Centennial College Chapter, WIE IEEE Toronto Location: Room B3-09 Centennial College, Progress Campus 941 Progress Ave., Toronto, Ontario, M1G 3T8 Contact: Reza Dibaj Abstract: At the beginning of the workshop, we quickly recap what we did in the previous session and knowing more about the definition of good features in our datasets. To continue our journey that we have started in the previous workshop, we will dive deeper into ML by applying our Decision Tree algorithm on a classical iris dataset. We will thoroughly practice training and testing data, using a step-by-step hands-on. Finally, we will use visualization tools to show what is happening under the hood in a decision tree and how it works as one of the most interpretable algorithms in ML.