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 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: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:20220503T180000 DTEND;TZID=America/New_York:20220503T190000 DTSTAMP:20260524T171125 CREATED:20220502T171646Z LAST-MODIFIED:20220504T073258Z UID:10000346-1651600800-1651604400@www.ieeetoronto.ca SUMMARY:DDoS Detection System – Students Research in ML and DL at Durham College DESCRIPTION:The research goal is to implement different machine learning algorithms to detect any DDoS (Distributed Denial of Service) attacks using the UNSW-NB15 dataset. We started by going through the data description and finding null values in our features. After that we dropped the ‘id’ column. \nWe have used the UNSW-15 dataset for AI-based DDOS detection systems. \nThe UNSW-15 dataset has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic. It contains different attacks\, including DoS\, worms\, Backdoors etc. The raw network packets of the UNSW-NB 15 datasets are created by the IXIA Perfect Storm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) for generating a hybrid of real modern normal activities and synthetic contemporary attack behaviours. We incorporated different feature selection methods for dropping insignificant features followed by the implementation of 6 classification algorithms\, namely Naive Bayes\, Random Forest\, Decision Tree\, KNN\, Logistic Regression and SVM. \nSpeaker(s): Minu Ahlawat\, Dwij Dua\, Megha Garg\, Taxil Savani \nRegister: https://events.vtools.ieee.org/m/312339 URL:https://www.ieeetoronto.ca/event/ddos-detection-system-students-research-in-ml-and-dl-at-durham-college/ LOCATION:toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312339 CATEGORIES:Magnetics,Women in Engineering ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220501T180000 DTEND;TZID=America/New_York:20220501T190000 DTSTAMP:20260524T171125 CREATED:20220502T171452Z LAST-MODIFIED:20220502T171549Z UID:10000525-1651428000-1651431600@www.ieeetoronto.ca SUMMARY:Sentiment Analysis on Twitter Data – Students Research in ML and DL at Durham College DESCRIPTION:The rise of digitalization and the advent of social media and e-commerce have generated an abundance of data than before. Natural Language Processing (NLP) is a significant branch of artificial intelligence that helps the machine interpret human languages and perform the desired task by analyzing the semantics\, content\, and pattern. Sentiment analysis is the most common technique in Natural Language Processing used to determine the underlying sentiments of a text. This technique is currently in place for different Business Organizations to analyze their brand’s market value\, brand reputation\, and customer perception of new brand/new change. Businesses use social media channels to cater to their customer service\, and people use social media to express/share their wide range of opinions or experiences about a product/brand. These opinions and experiences reflect the real-time sentiments of a customer. Sentiment analysis will help businesses designing an effective marketing campaign\, better customer satisfaction\, boost sales\, help improve customer experience\, understand customer perception to change and the brand’s market reputation. The customer views expressed on Twitter\, Facebook\, and other online forums are forming the base of customer strategy for brands worldwide. Businesses are opting to shift their traditional customer feedback analysis method to text classification since people prefer to post the genuine reviews on the internet. Analyzing the underlying sentiments in the text will help the business to understand their customers’ voices and their brand reputation in the market in real-time. Sentiment analysis will help the businesses designing an effective marketing campaign\, better customer satisfaction\, boost sales\, help improve customer experience\, understand customer perception to change and the brand’s market reputation. Twitter sentiment analysis aims to classify text into positive/negative based on its underlying semantics. \nSpeaker(s): Akhil Mathew\, Anmol Wadera\, Deepan Ellenti Padmanabhan\, Saketh Vemula\, Sivaramakrishna Malakalapalli \nRegister: https://events.vtools.ieee.org/m/312338 URL:https://www.ieeetoronto.ca/event/sentiment-analysis-on-twitter-data-students-research-in-ml-and-dl-at-durham-college/ LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312338 CATEGORIES:Magnetics,Women in Engineering ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220428T180000 DTEND;TZID=America/New_York:20220428T190000 DTSTAMP:20260524T171125 CREATED:20220425T202839Z LAST-MODIFIED:20220429T070737Z UID:10000524-1651168800-1651172400@www.ieeetoronto.ca SUMMARY:Text Summarization of Transcripts from Online Meetings – Students Research in ML and DL at Durham College DESCRIPTION:Text Summarization is a technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful information without losing the overall meaning. It aims to transform lengthy documents into shortened versions\, which could be difficult and costly to undertake if done manually. With the current explosion of data circulating in digital space\, primarily unstructured textual data\, there is a need to develop tools that allow people to get insights from them quickly. In situations where it is essential to keep track of what is being spoken\, such as during an online lecture\, taking notes is a popular activity used by many. The art of notetaking does not involve making notes of every single word that is spoken but comprehensive outlines of what is discussed. The key to good notetaking lies in making concise yet informative summaries. In this seminar\, we will be discussing how we have tried to address the difficulties of notetaking by building an application that produces notes based on transcripts generated by the Automatic Speech Recognition (ASR) technology of the meeting platforms. We experimented with six summarization models for this application\, including transformer-based models pre-trained on large corpora. The datasets used for this application are the transcripts dataset acquired from online meeting platforms and the Extreme Summarization (XSum) dataset. We evaluated the models using Rouge metrics (Rouge-1\, Rouge-2\, and Rouge-L) and selected the best-performing model as the final model. We have built a bot that utilizes Telegram’s API and shares the generated summaries via group chat with the users. \nSpeaker(s): Manoj Varma Alluri\, Navaneeth Jawahar\, Sharath Kumar Prabhu\, Jeel Jani\, Shravya Sandupata\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312337 URL:https://www.ieeetoronto.ca/event/text-summarization-of-transcripts-from-online-meetings-students-research-in-ml-and-dl-at-durham-college/ LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312337 CATEGORIES:Magnetics,Women in Engineering ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220427T180000 DTEND;TZID=America/New_York:20220427T190000 DTSTAMP:20260524T171125 CREATED:20220425T202533Z LAST-MODIFIED:20220429T070736Z UID:10000522-1651082400-1651086000@www.ieeetoronto.ca SUMMARY:Credit Card Fraud Detection – Students Research in ML and DL at Durham College DESCRIPTION:With the new trend of Online Shopping and Online Platforms for transactions\, the number of Credit Card based transactions increased tremendously. However\, there have been a lot of cases where illegal use of Debit/Credit Cards for making Fraudulent Transactions. Credit card companies have been paying a lot of attention to providing the best service for their customers by having process enhancements and pro-actively looking into transactions before making them through. Global financial losses related to payment cards are estimated to reach $34.66 billion in 2022\, according to The Nilson Report\, a newsletter that tracks the payment industry. Related to the negative impacts of credit card fraud activities\, and financial and product losses\, it’s easy for merchants and users to feel victimized and helpless. Machine Learning Models can work well in detecting such Fraudulent actions when they are trained on a large quantity of historical data and then fine-tuned depending on validation and evaluation metrics. \nSpeaker(s): Priyanka Singh\, Devy Ratnasari\, Gopika Shaji\, Oluwole Ayodele\, Saurav Bisht\,\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312336 URL:https://www.ieeetoronto.ca/event/credit-card-fraud-detection-students-research-in-ml-and-dl-at-durham-college/ LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312336 CATEGORIES:Magnetics,Women in Engineering ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220426T180000 DTEND;TZID=America/New_York:20220426T190000 DTSTAMP:20260524T171125 CREATED:20220425T202413Z LAST-MODIFIED:20220426T234759Z UID:10000521-1650996000-1650999600@www.ieeetoronto.ca SUMMARY:Fake News Detection – Students Research in ML and DL at Durham College DESCRIPTION:The term “fake news” was pretty much unknown and unpopular a few decades ago\, but it has emerged as a massive monster in the digital era of social media. Fake news is spreading like wildfire these days\, and people share it without confirming it. Often\, it is to promote or enforce specific views\, and it is carried out through political agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms. \nIn this digital age\, it is not easy to tackle the spread of fake news\, where thousands of information-sharing sites via fake news or misinformation can be shared. It has become a greater issue as AI advances\, bringing with it artificial bots that may be used to create and propagate fake news. The problem is critical because many individuals believe anything they read on the internet\, and those who are inexperienced or new to digital technologies are vulnerable to being misled. Fraud is another issue that can arise as a result of spam or harmful emails and communications.\nFake news has grown in popularity and spread as a result of recent political events. Humans are inconsistent\, if not outright terrible detectors of fake news\, as evidenced by the pervasive effects of the widespread onset of fake news. As a result\, efforts have been made to automate detecting fake news. The most prominent of these attempts are “blacklists” of unreliable sources and authors. While these technologies are useful we need to account for more complex instances when trusted sources and authors leak fake news in order to provide a complete end-to-end solution. As a result\, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patterns that distinguish fake and true news. The outcomes of this project show that machine learning can be effective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in the visual representation of the classification decision. We aim to give users the ability to classify news as fake or real and verify the website legitimacy that published it. \nSpeaker(s): Roshna Babu\, Abraham Mathew\, Neha Joseph\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334 URL:https://www.ieeetoronto.ca/event/fake-news-detection-students-research-in-ml-and-dl-at-durham-college/ LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334 CATEGORIES:Magnetics,Women in Engineering ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220310T160000 DTEND;TZID=America/New_York:20220310T171500 DTSTAMP:20260524T171125 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:20260524T171125 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:20260524T171125 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:20260524T171125 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:20260524T171125 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:20260524T171125 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:20260524T171125 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:20260524T171125 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:20260524T171125 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