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machine-learning-ex1 (0, 2016-10-18)
machine-learning-ex1\Images (0, 2016-10-18)
machine-learning-ex1\Images\plot1.png (22467, 2016-10-18)
machine-learning-ex1\Images\plot2.png (26933, 2016-10-18)
machine-learning-ex1\Images\plot3.png (86019, 2016-10-18)
machine-learning-ex1\Images\plot4.png (38531, 2016-10-18)
machine-learning-ex1\Rfunctions (0, 2016-10-18)
machine-learning-ex1\Rfunctions\computeCost.R (135, 2016-10-18)
machine-learning-ex1\Rfunctions\computeCostMulti.R (139, 2016-10-18)
machine-learning-ex1\Rfunctions\featureNormalize.R (480, 2016-10-18)
machine-learning-ex1\Rfunctions\gradientDescent.R (643, 2016-10-18)
machine-learning-ex1\Rfunctions\normalEqn.R (127, 2016-10-18)
machine-learning-ex1\Rfunctions\pinv.R (39, 2016-10-18)
machine-learning-ex1\Rfunctions\plotData.R (175, 2016-10-18)
machine-learning-ex1\Rfunctions\warmUpExercise.R (57, 2016-10-18)
machine-learning-ex1\ex1.pdf (453500, 2016-10-18)
machine-learning-ex1\ex1data1.txt (1359, 2016-10-18)
machine-learning-ex1\machine-learning-ex1.Rmd (4362, 2016-10-18)

# Machine-Learning-by-Stanford-University The MOOC: Machine Learning, from Stanford University on [Coursera](https://www.coursera.org/learn/machine-learning), covers machine learning, data mining,and statistical pattern recognition at broad level. More specifically, topics include supervised learning, unsupervised learning, best practices in machine learning, case studies and application of learning algorithms for building smart robots, text understanding, computer vision, medical informatics and more. Total enrollment since the courses launch in October 2011 is over one million students, making the MOOC the most well enrolled in the history of online education! The course, which is archived, and which led to the founding of Coursera, is taught by [Andrew Ng](http://www.andrewng.org). Andrew Ng is an associate professor of Computer Science at Stanford and Chief Scientist of Baidu. He co-founded Coursera with Daphne Koller, another Stanford computer science professor. ## Topics covered in the course and assignments 1. Linear regression, cost function and normalization 2. Gradient descent and advanced optimization 3. Multiple linear regression and normal equation 4. Logistic regression, decision boundary and multi-class classification 5. Over-fitting and Regularization 6. Neural Network non-linear classification 7. Model validation, diagnosis and learning curves 8. System design, prioritizing and error analysis 9. Support vector machine (SVM), large margin classification and SVM kernels (linear and Gaussian) 10. K-Means clustering 11. Principal component analysis (PCA) 12. Anomaly detection, supervised learning 13. Recommender systems, Collaborative filtering 14. Large scale machine learning, stochastic and mini-batch gradient descent, online learning, map reduce ## Introduction The course is offered in Octave/Matlab. R has been my preferred programming language since last couple of years and I'm not sure I would want to learn Octave just for the purpose of completing this course. This repository is a R equivalent for the course.

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