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:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20210314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20211107T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20220313T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20221106T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20230312T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20231105T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20240310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20241103T060000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20200101T000000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:America/Toronto BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20210314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20211107T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20220313T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20221106T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20230222T160000 DTEND;TZID=America/New_York:20230222T170000 DTSTAMP:20260416T063828 CREATED:20230130T112919Z LAST-MODIFIED:20230402T172827Z UID:10000601-1677081600-1677085200@www.ieeetoronto.ca SUMMARY:Overview of the Features and Functionalities of Modern Low Voltage AC Variable Frequency Drives DESCRIPTION:Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on The Features and Functionalities of Modern Low Voltage AC Variable Frequency Drives\, presented by Hossein Karimian\, P.Eng.\nWednesday\, February 22\, 2023\, @ 4:00 – 5:00 PM\nAbstract: Variable speed drives have opened numerous opportunities to improve productivity\, efficiency\, saving engineering time and costs.\nThey are effectively utilized in a wide range of applications in industries such as: fans\, pumps and compressors\, conveyors\, compressors\, mixers\, grinders\, saws\, lifts\, blowers\, agitators\, centrifuge…etc.\nDepending on the application\, the appropriate variable frequency drives can be selected and incorporated in the automation concept.\nHossein Karimian’s talk will present advantages of Siemens low voltage SINAMICS VFD while discussing its performance\, and innovative design.\nSpeaker(s): Hossein Karimian\, P.Eng.\, FS Eng.\,\nVirtual: https://events.vtools.ieee.org/m/345481 URL:https://www.ieeetoronto.ca/event/overview-of-the-features-and-functionalities-of-modern-low-voltage-ac-variable-frequency-drives/ LOCATION:Virtual: https://events.vtools.ieee.org/m/345481 CATEGORIES:Instrumentation & Measurement END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20221129T163000 DTEND;TZID=America/New_York:20221129T173000 DTSTAMP:20260416T063828 CREATED:20221103T163432Z LAST-MODIFIED:20230402T175110Z UID:10000579-1669739400-1669743000@www.ieeetoronto.ca SUMMARY:The Critical Role of Computational Modeling in Future Diagnostic\, Monitoring\, and Predictive Tools for Cardiovascular Diseases DESCRIPTION:Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on The Role of Computational Modeling in Future Diagnostic\, Monitoring\, and Predictive Tools for Cardiovascular Diseases \, presented by Dr. Zahra K. Motamed.\nTuesday\, November 29\, 2022 @ 4:30 – 5:30 PM\nAbstract: The main functions of the cardiovascular system are to transport\, control and maintain blood flow in the entire body. Abnormal hemodynamics greatly alters this tranquil picture\, leading to initiation and progression of disease. These abnormalities are often manifested by disturbed fluid dynamics (local hemodynamics)\, and in many cases by an increase in the heart workload (global hemodynamics). Hemodynamics quantification can be greatly useful for accurate and early diagnosis\, but we still lack proper diagnostic methods for many cardiovascular diseases because the hemodynamics analysis methods that can be used as engines of new diagnostic tools are not well developed yet. Furthermore\, as most interventions intend to recover the healthy condition\, the ability to monitor and predict hemodynamics following particular interventions can have significant impacts on saving lives. Despite remarkable advances in medical imaging\, imaging on its own is not predictive. Predictive methods are rare. They are extensions of diagnostic methods\, enabling prediction of effects of interventions\, allowing timely and personalized interventions\, and helping critical clinical decision making about life-threatening risks based on quantitative data.\nDr. Motamed and her team has developed innovative non-invasive image-based patient-specific diagnostic\, monitoring and predictive computational-mechanics framework for patients with cardiovascular disease. Currently\, none of the above metrics can be obtained noninvasively in patients in clinics and when invasive procedures are undertaken\, the collected metrics cannot be by any means as complete as the results that Motamed lab’s framework provides.\nSpeaker(s): Zahra K. Motamed\, PhD \,\nVirtual: https://events.vtools.ieee.org/m/330836 URL:https://www.ieeetoronto.ca/event/the-critical-role-of-computational-modeling-in-future-diagnostic-monitoring-and-predictive-tools-for-cardiovascular-diseases/ LOCATION:Virtual: https://events.vtools.ieee.org/m/330836 CATEGORIES:Instrumentation & Measurement END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220816T160000 DTEND;TZID=America/New_York:20220816T170000 DTSTAMP:20260416T063828 CREATED:20220614T210749Z LAST-MODIFIED:20220817T134242Z UID:10000548-1660665600-1660669200@www.ieeetoronto.ca SUMMARY:How to Leverage Machine Learning Tools in Model Predictive Control Schemes DESCRIPTION:Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on Application of Machine Learning in Model Predictive Control\, presented by Dr. Meaghan Charest-Finn.\nTuesday\, August 16\, 2022 @ 4:00 – 5:00 PM \nAbstract: Model Predictive Control (MPC) algorithms provide a convenient entry point for machine learning methods as they are built around a system model. Furthermore\, these types of constrained control algorithms are robust\, well suited for Multiple-Input Multiple-Output (MIMO) Systems and Nonlinear Systems. In this talk we will discuss fundamental concepts of MPC and how the model component can be used to leverage Artificial Intelligence (AI) \nSpeaker(s): Meaghan Charest-Finn\, PhD \nRegister: https://events.vtools.ieee.org/m/317037 URL:https://www.ieeetoronto.ca/event/how-to-leverage-machine-learning-tools-in-model-predictive-control-schemes/ LOCATION:Virtual: https://events.vtools.ieee.org/m/317037 CATEGORIES:Instrumentation & Measurement END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220622T103000 DTEND;TZID=America/New_York:20220622T113000 DTSTAMP:20260416T063828 CREATED:20220606T203859Z LAST-MODIFIED:20220622T215910Z UID:10000536-1655893800-1655897400@www.ieeetoronto.ca SUMMARY:DEVELOPMENT OF AN 8X8 AUTONOMOUS SCALED ELECTRIC COMBAT VEHICLE DESCRIPTION:Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on Autonomous Electric Combat Vehicles\, presented by Prof. Zeinab El-Sayegh. \nAbstract: Current literature pertaining to multi-steerable mobile platforms and the progression of military vehicles in the past few decades suggest a lack of effort in pursuing advanced technologies in this joint area. As a result\, a novel 1:6 scaled 8×8 electric combat vehicle prototype that features eight independently driven and steerable wheels is designed and developed. The intent is to create a scaled model for future autonomous vehicle navigation and control research in off road terrains. Starting with the mechanical design\, this talk will discuss the details of the chassis\, suspension\, driving and steering systems development. The electronics necessary for vehicle actuation is implemented with custom nodes and topics created for hardware communication within the Robot Operating System (ROS). Lastly\, path planning and obstacle avoidance abilities are implemented to achieve autonomous navigation. The result of this work is a fully functional and instrumented robotic platform with a modular software architecture. Vehicle design analysis\, performance and autonomous navigation abilities are experimentally tested with promising results. \nThis talk will also cover the future of autonomous electric combat vehicles. \nSpeaker(s): Zeinab El-Sayegh\, PhD\, PEng \nRegister: https://events.vtools.ieee.org/m/316156 \nBiography: Dr. Zeinab El-Sayegh is an Assistant Professor in the Department of Automotive and Mechatronics Engineering. She completed her postdoctoral fellowship and Ph.D. in Mechanical Engineering at Ontario Tech University. She received her master’s degree in Mechanical Engineering from the University of Concordia\, Montreal. Formally\, worked at Volvo Group Trucks Technology Gothenburg\, Sweden as a vehicle analyst. And\, as a combustion analyst in Siemens aero-derivative gas turbines\, Montreal. Her research interests are related to on-road and off-road vehicle design\, and autonomous and hybrid electric vehicle simulation. She is involved in research related to tire-terrain interaction in cooperation with Volvo Group Truck Technology and NSERC. URL:https://www.ieeetoronto.ca/event/development-of-an-8x8-autonomous-scaled-electric-combat-vehicle/ LOCATION:Virtual: https://events.vtools.ieee.org/m/316156 CATEGORIES:Instrumentation & Measurement,Women in Engineering ORGANIZER;CN="Saba Sedghizadeh":MAILTO:s.sedghizadeh.CA@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220408T100000 DTEND;TZID=America/New_York:20220408T113000 DTSTAMP:20260416T063828 CREATED:20220330T182106Z LAST-MODIFIED:20220408T215623Z UID:10000515-1649412000-1649417400@www.ieeetoronto.ca SUMMARY:Overview of Factory Automation Market in Canada DESCRIPTION:The goal of the session is to present a summary of the automation industry in Canada with a focus on factory automation. The following topics would be covered:\n– Active Industries\n– Market Structure\n– Key Players\n– Future Trends\n– Automation Jobs\n– Required Skills\n– Challenges \nSpeaker(s): Shahram Fahimi \nVirtual: https://events.vtools.ieee.org/m/309678 \nBiography: Shahram Fahimi is the Automation Manager of the Life Science group in SNC-Lavalin. He has over 20 years of experience in the automation industry with a focus on Factory and Process Automation. Shahram has contributed to many exciting automation projects such as the first battery line for Tesla in California using Trak technology. He has been graduated from Sharif University of Technology in 1998 with a Bachelor of Computer Engineering. URL:https://www.ieeetoronto.ca/event/overview-of-factory-automation-market-in-canada/ LOCATION:Virtual: https://events.vtools.ieee.org/m/309678 CATEGORIES:Instrumentation & Measurement ORGANIZER;CN="Saba Sedghizadeh":MAILTO:s.sedghizadeh.CA@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220310T160000 DTEND;TZID=America/New_York:20220310T171500 DTSTAMP:20260416T063828 CREATED:20220307T181656Z LAST-MODIFIED:20220307T181656Z UID:10000506-1646928000-1646932500@www.ieeetoronto.ca SUMMARY:Women in Leadership DESCRIPTION:“Women in Leadership”\, a collaboration between IEEE Toronto Section\, Gybo Robotics\, and Humber College. \nCo-sponsored by: Humber College \nSpeaker(s): Dr. Azadeh Yadollahi \nVirtual: https://events.vtools.ieee.org/m/306228 URL:https://www.ieeetoronto.ca/event/women-in-leadership/ LOCATION:Virtual: https://events.vtools.ieee.org/m/306228 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211214T170000 DTEND;TZID=UTC:20211214T183000 DTSTAMP:20260416T063828 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211207T170000 DTEND;TZID=UTC:20211207T183000 DTSTAMP:20260416T063828 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211204T180000 DTEND;TZID=UTC:20211204T200000 DTSTAMP:20260416T063828 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211130T170000 DTEND;TZID=UTC:20211130T183000 DTSTAMP:20260416T063828 CREATED:20211109T123535Z LAST-MODIFIED:20211230T092235Z UID:10000487-1638291600-1638297000@www.ieeetoronto.ca SUMMARY:Dimension Reduction & Maximum Likelihood: How to compress your data while retaining the key features 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: A randomly chosen bit string cannot be compressed at all. But if there is a pattern to it\, eg it represents an image\, then maybe it can be compressed. Each pixel of an image is specified by one (or three) real numbers. If an image has thousands/millions of pixels\, then each of these acts as a coordinate of the point where the image sits in a very high dimensional space. A set of such images then corresponds to a set of these points. We can understand the pattern of points/images as follows. Maximum Likelihood assumes that the given set of points/images were randomly chosen according a multi-dimensional normal distribution and then adjusts the parameters of this normal distribution in the way that maximizes the probability of getting the images that we have. The obtained parameters effectively fits an ellipse around the points/images in this high dimensional space. We then reduce the number of dimensions in our space by collapsing this ellipse along its least significant axises. Projecting each point/image to this lower dimensional space compresses the amount of information needed to represent each image. Virtual: https://events.vtools.ieee.org/m/289240 URL:https://www.ieeetoronto.ca/event/dimension-reduction-maximum-likelihood-how-to-compress-your-data-while-retaining-the-key-features/ LOCATION:Virtual: https://events.vtools.ieee.org/m/289240 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211125T180000 DTEND;TZID=UTC:20211125T200000 DTSTAMP:20260416T063828 CREATED:20211030T112020Z LAST-MODIFIED:20211225T090309Z UID:10000481-1637863200-1637870400@www.ieeetoronto.ca SUMMARY:IEEE CIC x GMU Indie Game Jam: UI & Game Controller 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) – Introduction to the Package Manager & Post Processing package (10 minutes) ● Apply post processing effects to camera (20 minutes) – Implement camera shaking (20 minutes) – Break (10 minutes) – Building our project (10 minutes) ● QnA (40 minutes) Virtual: https://events.vtools.ieee.org/m/287756 URL:https://www.ieeetoronto.ca/event/ieee-cic-x-gmu-indie-game-jam-ui-game-controller/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287756 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211123T170000 DTEND;TZID=UTC:20211123T183000 DTSTAMP:20260416T063828 CREATED:20211030T112020Z LAST-MODIFIED:20211223T084823Z UID:10000480-1637686800-1637692200@www.ieeetoronto.ca SUMMARY:Reinforcement Learning Game Tree / Markoff Chains 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: At the risk of being non-standard\, Jeff will tell you the way he thinks about this topic. Both “Game Trees” and “Markoff Chains” represent the graph of states through which your agent will traverse a path while completing the task. Suppose we could learn for each such state a value measuring “how good” this state is for the agent. Then competing the task in an optimal way would be easy. If our current state is one within which our agent gets to choose the next action\, then she will choose the action that maximizes the value of our next state. On the other hand\, if our adversary gets to choose\, he will choose the action that minimizes this value. Finally\, if our current state is one within which the universe flips a coin\, then each edge leaving this state will be labeled with the probability of taking it. Knowing that that is how the game is played\, we can compute how good each state is. The states in which the task is complete is worth whatever reward the agent receives in the said state. These values somehow trickle backwards until we learn the value of the start state. The computational challenge is that there are way more states then we can ever look at. Speaker(s): Prof. Jeff Edmonds\, Virtual: https://events.vtools.ieee.org/m/287737 URL:https://www.ieeetoronto.ca/event/reinforcement-learning-game-tree-markoff-chains/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287737 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211118T180000 DTEND;TZID=UTC:20211118T200000 DTSTAMP:20260416T063828 CREATED:20211030T112020Z LAST-MODIFIED:20211218T081851Z UID:10000479-1637258400-1637265600@www.ieeetoronto.ca SUMMARY:IEEE CIC x GMU Indie Game Jam: Enemy & Enemy AI 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 enemy object & its components (10 minutes): ○ Rigidbody 2D (kinematic) ○ Box Collider 2D ○ Sprite Renderer – Add enemy script & implement enemy random generation (20 minutes) ● Implement enemy movement & shooting behaviour (20 minutes) – Break (10 minutes) – Implement bullet damaging player & enemy (20 minutes) – Add game controller script & implement enemy spawning (20 minutes) ● Add basic player resources (health\, ammo) & player score (10 minutes) Virtual: https://events.vtools.ieee.org/m/287749 URL:https://www.ieeetoronto.ca/event/ieee-cic-x-gmu-indie-game-jam-enemy-enemy-ai/ LOCATION:Virtual: https://events.vtools.ieee.org/m/287749 CATEGORIES:Instrumentation & Measurement,Women in Engineering END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20211116T170000 DTEND;TZID=UTC:20211116T183000 DTSTAMP:20260416T063828 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