BEGIN:VCALENDAR VERSION:2.0 PRODID:-//IEEE Toronto Section - ECPv6.15.17//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://www.ieeetoronto.ca X-WR-CALDESC:Events for IEEE Toronto Section REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20200101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=UTC:20211102T170000 DTEND;TZID=UTC:20211102T183000 DTSTAMP:20261104T204745 CREATED:20211027T103902Z LAST-MODIFIED:20211202T020122Z UID:10000318-1635872400-1635877800@www.ieeetoronto.ca SUMMARY:Intro to the Mathematics in Machine Learning DESCRIPTION: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 URL:https://www.ieeetoronto.ca/event/intro-to-the-mathematics-in-machine-learning/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287252 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211104T180000 DTEND;TZID=UTC:20211104T200000 DTSTAMP:20261104T204745 CREATED:20211030T112018Z LAST-MODIFIED:20211204T070601Z UID:10000320-1636048800-1636056000@www.ieeetoronto.ca SUMMARY:IEEE CIC x Ryerson GMU Indie Game Jam: The Basics & Tile System DESCRIPTION: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 URL:https://www.ieeetoronto.ca/event/ieee-cic-x-ryerson-gmu-indie-game-jam-the-basics-tile-system/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287738 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211109T170000 DTEND;TZID=UTC:20211109T183000 DTSTAMP:20261104T204745 CREATED:20211028T105020Z LAST-MODIFIED:20211209T073407Z UID:10000319-1636477200-1636482600@www.ieeetoronto.ca SUMMARY:Algebra Review: How does one best think about all of these numbers 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. — 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 URL:https://www.ieeetoronto.ca/event/algebra-review-how-does-one-best-think-about-all-of-these-numbers/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287446 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211111T180000 DTEND;TZID=UTC:20211111T190000 DTSTAMP:20261104T204745 CREATED:20210916T124947Z LAST-MODIFIED:20211211T074915Z UID:10000459-1636653600-1636657200@www.ieeetoronto.ca SUMMARY:Writing Attention-Grabbing Resumes & Cover Letters DESCRIPTION:Unclear about how to tailor a resume to industry jobs? Want to learn how to describe your accomplishments in an impactful manner? In this webinar\, you will learn how to gain the attention of hiring managers with well-written resumes and cover letters! Virtual: https://events.vtools.ieee.org/m/281921 URL:https://www.ieeetoronto.ca/event/writing-attention-grabbing-resumes-cover-letters/ LOCATION:Virtual: https://events.vtools.ieee.org/m/281921 CATEGORIES:Aerospace & Electronic Systems,Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211111T180000 DTEND;TZID=UTC:20211111T200000 DTSTAMP:20261104T204745 CREATED:20211030T112019Z LAST-MODIFIED:20211211T074915Z UID:10000321-1636653600-1636660800@www.ieeetoronto.ca SUMMARY:IEEE CIC x GMU Indie Game Jam: Player & Bullet DESCRIPTION: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. – Quick review of last week’s progress (10 minutes) – Add player game object & its components (10 minutes): ○ Rigidbody 2D ○ Box Collider 2D ○ Sprite Renderer ○ Shadow – Add player script & implement basic movement\, shadow positioning (10 minutes) ● Implement player mouse rotation (10 minutes) – Introduction to the particle effects system & implement player trailing effect (20 minutes) ● Break (10 minutes) – Prevent player from going off screen (10 minutes) – Add bullet object & its components (10 minutes): ○ Rigidbody 2D ○ Box Collider 2D ○ Sprite Renderer – Add bullet script & implement bullet flying movement (10 minutes) ● Implement bullet shooting (20 minutes) Virtual: https://events.vtools.ieee.org/m/287748 URL:https://www.ieeetoronto.ca/event/ieee-cic-x-gmu-indie-game-jam-player-bullet/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287748 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211116T170000 DTEND;TZID=UTC:20211116T183000 DTSTAMP:20261104T204745 CREATED:20211030T112019Z LAST-MODIFIED:20211216T081306Z UID:10000478-1637082000-1637087400@www.ieeetoronto.ca SUMMARY:Generalizing from Training Data 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. Longer Abstract: There is some theory. If a machine is found that gives the correct answers on the randomly chosen training data without simply memorizing\, then we can prove that with high probability this same machine will also work well on never seen before instances drawn from the same distribution. The easy proof requires D>m\, where m is the number of bits needed to describe your learned machine and D is the number of train data items. A much harder proof (which we likely won’t cover) requires only D>VC\, where VC is VC-dimension (Vapnik–Chervonenkis) of your machine. The second requirement is easier to meet because VC