Events

--- 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

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

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<m. Speaker(s): Prof. Jeff Edmonds, Virtual: https://events.vtools.ieee.org/m/287720

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

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

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