Lempel-Ziv algorithm
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Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)
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Algorithm Analysis, Evaluating the Performance, Analysis of Codes: Statement Counts, Another Example (Statement Count Contd.), Comparing Algorithm, Big-O Notation, Big-O to Predict the Time of Execution, Best/Worst/Average Case, Analysis of Recursive Algorithms, Another Example : Towers of Hanoi, A Tabulation for Different Algorithms, Growth Patterns, Application of Algorithm Analysis to Sorting, Selection Sort, Selection Sort Code
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Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example - Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem
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Handout Information, Section Sign-up, Karel Commands, An Algorithm vs Program, Syntax of a Karel Program, Running a Karel Program, Creating Methods, SuperKarel, A for Loop, A While Loop, Karel Conditions, If Statement, Putting it All Together
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Joint Space Dynamics, Newton-Euler Algorithm, Inertia Tensor, Example, Newton-Euler Equations, Lagrange Equations, Equations of Motion
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Kernels, Mercer's Theorem, Non-linear Decision Boundaries and Soft Margin SVM, Coordinate Ascent Algorithm, The Sequential Minimization Optimization (SMO) Algorithm, Applications of SVM
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Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD) Implementation, Independent Component Analysis (ICA), The Application of ICA, Cumulative Distribution Function (CDF), ICA Algorithm, The Applications of ICA
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Markov sources and Lempel-Ziv universal codes
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Partially Observable MDPs (POMDPs), Policy Search, Reinforce Algorithm, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning
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This course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451, is offered in the spring. Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM...more
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This course is the second of a two-term sequence. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of Principles of Digital Communication I and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-d...more
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Introduction to Lecture's material - Searching, Sorting and Algorithmic Efficiency, Searching, Linear Search, Efficiency of Linear Search, Binary Search, Efficiency of Binary Search, Comparing Search Efficiencies, Sorting, The Selection Sort Algorithm, Efficiency of Selection Sort, The Radix Sort Algorithm
