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Algorithm implementations and homework solutions for the Stanford's online courses http://www.emilmont.net/doku.php?id=a…
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This project contains my algorithm implementations for the following online courses: * Introduction to Artificial Intelligence: http://www.ai-class.com * Overview of AI, Search * Statistics, Uncertainty, and Bayes networks * Machine Learning * Logic and Planning * Markov Decision Processes and Reinforcement Learning * Hidden Markov Models and Filters * Adversarial and Advanced Planning * Image Processing and Computer Vision * Robotics and robot motion planning * Natural Language Processing and Information Retrieval * Introduction to Machine Learning: http://www.ml-class.com * Linear Regression, Gradient Descent * Logistic Regression * Multi-class Classification, Neural Networks * Neural Networks Learning * Regularized Linear Regression and Bias vs Variance, Polynomial Regression * Support Vector Machines, Classifiers * K-means Clustering and Principal Component Analysis * Anomaly Detection and Recommender Systems * Artificial Intelligence for Robotics: http://www.udacity.com/course/cs373 * Localization: Monte-Carlo, Kalman Filters, Particle Filters. * Planning and search: A* search, dynamic programming. * Controls: PID, parameters optimization, smoothing. * Simultaneous localization and mapping (SLAM). * Computational Investing, Part I: https://www.coursera.org/course/compinvesting1 * Data Analysis with Python pandas and QSTK * Event profiling * Portfolio Optimization * Natural Language Processing: https://www.coursera.org/course/nlangp * Hidden Markov models, and tagging problems: Viterbi algorithm In observance of the honor code, I will submit my code to this repository only after the correspondent homework assignments are officially closed.