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DTSTART;TZID=UTC:20211204T180000
DTEND;TZID=UTC:20211204T200000
DTSTAMP:20261108T071254
CREATED:20211030T112020Z
LAST-MODIFIED:20220105T233546Z
UID:10000482-1638640800-1638648000@www.ieeetoronto.ca
SUMMARY:IEEE CIC x GMU Indie Game Jam: Finishing up & QnA
DESCRIPTION:This series of 5 beginner friendly workshops will teach students how to create their own indie game in Unity. \nWe will teach the building blocks and best practices to create a shooter including creating the player\, creating enemies\, collectibles\, effects\, and more! \nAll 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. \n\nQuick review of last week’s progress (10 minutes)\nIntroduction to the Package Manager & Post Processing package (10 minutes) ● Apply post processing effects to camera (20 minutes)\nImplement camera shaking (20 minutes)\nBreak (10 minutes)\nBuilding our project (10 minutes)\nQnA (40 minutes)\n\nVirtual: https://events.vtools.ieee.org/m/287758
URL:https://www.ieeetoronto.ca/event/ieee-cic-x-gmu-indie-game-jam-finishing-up-qna/
LOCATION:Virtual: https://events.vtools.ieee.org/m/287758
CATEGORIES:Instrumentation & Measurement,Women in Engineering
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211207T170000
DTEND;TZID=UTC:20211207T183000
DTSTAMP:20261108T071254
CREATED:20211109T123536Z
LAST-MODIFIED:20220105T233027Z
UID:10000488-1638896400-1638901800@www.ieeetoronto.ca
SUMMARY:Generative Adversarial Networks: Used for understanding and producing a random data item
DESCRIPTION: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. \nLonger Abstract: Suppose you have a distributions of random images of cats. Suppose you want to learn a neural network that takes uniformly random bits as input and outputs an image of a cat according to this same distribution. One fun thing is that this neural network won’t be perfect and hence it will output images of “cats” that it has never seen before. Also you can make small changes in the network input bits and see how it changes the resulting image of a cat. The way we do this is with Generative Adversarial Networks. This is formed by having two competing agents. The task of the first agent\, as described above\, is to output random images of cats. The task of the second is to discern whether a given image was produced by the true random distribution or by the first agent. By competing\, they learn. If we have more time in the talk then we will talk about Convolutional & Recurrent Networks which are used for learning images and sound that are invariant over location and time. \nVirtual: https://events.vtools.ieee.org/m/289241
URL:https://www.ieeetoronto.ca/event/generative-adversarial-networks-used-for-understanding-and-producing-a-random-data-item/
LOCATION:Virtual: https://events.vtools.ieee.org/m/289241
CATEGORIES:Instrumentation & Measurement,Women in Engineering
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211214T170000
DTEND;TZID=UTC:20211214T183000
DTSTAMP:20261108T071254
CREATED:20211109T123536Z
LAST-MODIFIED:20220105T094814Z
UID:10000489-1639501200-1639506600@www.ieeetoronto.ca
SUMMARY:Ethics: How might the machine learning make the world a better place? How might it make the world worse?
DESCRIPTION:How might the machine learning make the world a better place?\nHow might it make the world worse?\nI have some thoughts. Likely you do too.\nVirtual: https://events.vtools.ieee.org/m/289243
URL:https://www.ieeetoronto.ca/event/ethics-how-might-the-machine-learning-make-the-world-a-better-place-how-might-it-make-the-world-worse/
LOCATION:Virtual: https://events.vtools.ieee.org/m/289243
CATEGORIES:Instrumentation & Measurement,Women in Engineering
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