CSC 311: Introduction to Machine Learning Lecture 1 – Introduction Anthony Bonner & Based on slides by Amir-massoud Farahmand & Emad A.M. Andrews Intro ML (UofT) CSC311-Lec1 1 / 53 This course Broad introduction to machine learning First half: algorithms and principles for supervised learning nearest neighbors, decision trees, ensembles, linear regression, logistic […]
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CSC 311: Introduction to Machine Learning Lecture 8 – Reinforcement Learning University of Toronto Intro ML (UofT) CSC311-Lec8 1 / 46 Reinforcement Learning Problem In supervised learning, the problem is to predict an output t given an input x. But often the ultimate goal is not to predict, but to make decisions, i.e., take actions.
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Cloud and Big Data IBM Research Course Objective Copyright By PowCoder代写 加微信 powcoder § Graduate level course on Cloud Computing – Focus is on learning how to design, build, deploy and manage extremely large scale systems and applications leveraging Cloud – Building blocks and design patterns in designing backend of typical Internet Scale application –
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5G Networks 5G is the fifth and latest generation of cellular networks that has just started to roll out in 2019-2020. While the previous four generations mainly sought to improve the data rate and capacity of the cellular systems, 5G is designed to improve several other aspects of communications and connectivity beyond the data rates.
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EECS 70 Discrete Mathematics and Probability Theory Fall 2021 Error Correcting Codes In this note, we will discuss the problem of transmitting messages across an unreliable communication chan- nel. The channel may cause some parts of the message (“packets”) to be lost, or dropped; or, more seriously, it may cause some packets to be corrupted.
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EECS 70 Discrete Mathematics and Probability Theory Fall 2021 Polynomials Polynomials constitute a rich class of functions which are both easy to describe and widely applicable in topics ranging from Fourier analysis, cryptography and communication, to control and computational geom- etry. You’ve seen them earlier in many contexts like Taylor approximation and other contexts in
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Introduction to Machine Learning Introduction to Collaborative Filtering Prof. Kutty spectral clustering review Copyright By PowCoder代写 加微信 powcoder weighted undirected Data → Graph: Adjacency matrix, ” more similar E 1ktvlog 0 I high value sin Eci act Given !! = $̅ ” compute similarity between each pair of datapoints e.g., Gaussian Sim I aces kernel
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Linear programming duality This week we cover the fascinating topic of linear programming du- ality. We will learn that every minimization program has associated a maximization program that has the same value. This surprising connection will allow us to efficiently certify the optimality or the infeasibility of a given linear program. 4.1 Bounding the value
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Minimum weight submodular cover This week we study the minimum weight submodular cover prob- lem, a generalizations of the minimum weight set cover problem. 12.1 Minimum set cover The input of the minimum weight set cover problem is a collection of subsets S = {S1,…,Sm} of some ground set U and a weight function w
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Linear programming applications This week we will showcase a few extra surprising applications of linear programming: How to play zero-sum games, and how to ex- ploit the properties of basic solutions to solve discrete optimization problems. 5.1 Playing zero-sum games The easiest way to introduce zero-sum games is to look at a classic game played
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