9/2/2023 0 Comments Relative entropy![]() The second half will develop information theoretic bounds on the generalization error in statistical learning. The first half of the course will focus on one-shot approaches in multiuser information theory and discuss some applications to machine learning. This course is designed for students with a background in communication systems and information theory, interested in doing research in machine learning. Top ECE1504H Statistical Learning Exclusions: ECE421H, CSC411H1/CSC2515H, ECE1513H This course deals with fundamental limits on communication, including the following topics: entropy, relative entropy and mutual information: entropy rates for stochastic processes differential entropy data compression the Kraft inequality Shannon-Fano codes Huffman codes arithmetic coding channel capacity discrete channels the random coding bound and its converse the capacity of Gaussian channels the sphere-packing bound coloured Gaussian noise and water-filling rate-distortion theory the rate-distortion function multiuser information theory. Topics include algebraic coding theory: finite fields, linear codes, cyclic codes, BCH codes and decoding, Reed-Solomon codes iterative decoding: codes defined on graphs, the sum-product algorithm, low-density parity-check codes, turbo codes. This course provides an introduction to error control techniques, with emphasis on decoding algorithms. Topics include random vectors, random convergence, random processes, specifying random processes, Poisson and Gaussian processes, stationarity, mean square derivatives and integrals, ergodicity, power spectrum, linear systems with stochastic input, mean square estimation, Markov chains, recurrence, absorption, limiting and steady-state distributions, time reversibility, and balance equations. Introduction to the principles and properties of random processes, with applications to communications, control systems, and computer science. We present three different variants of our algorithm, designed to be suitable for a wide variety of real world robot learning tasks and evaluate our algorithms in two real robot learning scenarios as well as several simulations and comparisons.Note: The course catalogues, the SGS Calendar, and ACORN list all graduate courses associated with ECE – please note that not all courses will be offered every year. In order to efficiently share experience with all sub-policies, also called inter-policy learning, we treat these sub-policies as latent variables which allows for distribution of the update information between the sub-policies. We define the problem of learning sub-policies in continuous state action spaces as finding a hierarchical policy that is composed of a high-level gating policy to select the low-level sub-policies for execution by the agent. Real world settings are challenging due to large and continuous state-action spaces that are prohibitive for exhaustive sampling methods. However, this concept has only been partially explored for real world settings and complete methods, derived from first principles, are needed. Such task structures can be exploited by incorporating hierarchical policies that consist of gating networks and sub-policies. Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks that are strongly structured. Hierarchical Relative Entropy Policy SearchĬhristian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters 17(93):1−50, 2016. ![]()
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