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### Online Machine Learning Course Summary

 Course: Machine Learning Course Instructor: Prof. Andrew Ng, Stanford University Course Provider: Coursera.org Course Duration: 10 weeks Course Performance: Earned 100% of all points possible in course (including optional problems) Topic Summary  1. Introduction  2. Linear Regression with One Variable  3. Linear Algebra Review  4. Linear Regression with Multiple Variables  5. Octave Tutorial  6. Logistic Regression  7. Regularization  8. Neural Networks: Representation  9. Neural Networks: Learning 10. Advice for Applying Machine Learning 11. Machine Learning System Design 12. Support Vector Machines 13. Clustering 14. Dimensionality Reduction 15. Anomaly Detection 16. Recommender Systems 17. Large Scale Machine Learning 18. Application Example: Photo OCR Detailed Topic Summary 1. Introduction What is Machine Learning? Supervised Learning Unsupervised Learning 2. Linear Regression with One Variable Model Representation Cost Function Gradient Descent Gradient Descent For Linear Regression 3. Linear Algebra Review 4. Linear Regression with Multiple Variables Multiple Features Gradient Descent for Multiple Variables Gradient Descent in Practice I - Feature Scaling Gradient Descent in Practice II - Learning Rate Features and Polynomial Regression Normal Equation Normal Equation Noninvertibility 5. Octave Tutorial Basic Operations Moving Data Around Computing on Data Plotting Data Control Statements: for, while, if statements Vectorization Working on and Submitting Programming Exercises 6. Logistic Regression Classification Hypothesis Representation Decision Boundary Cost Function Simplified Cost Function and Gradient Descent Advanced Optimization Multiclass Classification: One-vs-all 7. Regularization 8. Neural Networks: Representation Non-linear Hypotheses Neurons and the Brain Model Representation Multiclass Classification 9. Neural Networks: Learning Cost Function Backpropagation Algorithm Gradient Checking Random Initialization Putting It Together Autonomous Driving 10. Advice for Applying Machine Learning Evaluating a Hypothesis Model Selection and Train/Validation/Test Sets Diagnosing Bias vs. Variance Regularization and Bias/Variance Learning Curves 11. Machine Learning System Design Prioritizing What to Work On Error Analysis Error Metrics for Skewed Classes Trading Off Precision and Recall Data For Machine Learning 12. Support Vector Machines Optimization Objective Large Margin Intuition Mathematics Behind Large Margin Classification Kernels Using An SVM 13. Clustering Unsupervised Learning: Introduction K-Means Algorithm Optimization Objective Random Initialization Choosing the Number of Clusters 14. Dimensionality Reduction Motivation I: Data Compression Motivation II: Visualization Principal Component Analysis Problem Formulation Principal Component Analysis Algorithm Choosing the Number of Principal Components Reconstruction from Compressed Representation Advice for Applying PCA 15. Anomaly Detection Problem Motivation Gaussian Distribution Algorithm Developing and Evaluating an Anomaly Detection System Anomaly Detection vs. Supervised Learning Choosing What Features to Use Multivariate Gaussian Distribution Anomaly Detection using the Multivariate Gaussian Distribution 16. Recommender Systems Problem Formulation Content Based Recommendations Collaborative Filtering Collaborative Filtering Algorithm Vectorization: Low Rank Matrix Factorization Implementational Detail: Mean Normalization 17. Large Scale Machine Learning Learning With Large Datasets Stochastic Gradient Descent Mini-Batch Gradient Descent Stochastic Gradient Descent Convergence Online Learning Map Reduce and Data Parallelism 18. Application Example: Photo OCR Problem Description and Pipeline Sliding Windows Getting Lots of Data and Artificial Data Ceiling Analysis: What Part of the Pipeline to Work on Next