Algorithm implementations and homework solutions for the Stanford's online courses
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This project contains my algorithm implementations for the following online courses:
  * Introduction to Artificial Intelligence:
    * 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:
    * 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:
    * 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:
    * Data Analysis with Python pandas and QSTK
    * Event profiling
    * Portfolio Optimization
  * Natural Language Processing:
    * 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.