Author: Tal Kitron
Status: Finished

Repository for keeping track of Stanford Machine Learning by Coursera. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

1. Week 1: Linear Regression with One Variable & Linear Algebra
2. Week 2: Linear Regression with Multiple Variables & Octave/Matlab Tutorial.
3. Week 3: Logistic Regression & Regularization.
4. Week 4: Neural Networks: Representation.
5. Week 5: Neural Networks: Learning.
6. Week 6: Advice for Applying Machine Learning & Machine Learning System Design.
7. Week 7: Support Vector Machines.
8. Week 8: Unsupervised Learning & Dimensionality Reduction.
9. Week 9: Anomaly Detection & Recommender Systems.
10. Week 10: Large Scale Machine Learning.
11. Week 11: Application Example: Photo OCR

• Coursera - Stanford Machine Learning