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资源说明：Algorithm implementations and homework solutions for the Stanford's online courses
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.
本源码包内暂不包含可直接显示的源代码文件，请下载源码包。
