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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