/machine-learning

Materials from the Machine Learning course by Stanford

Primary LanguageMATLAB

Machine Learning course by Stanford

Syllabus

  • Week 1
    • Linear Regression with One Variable
      • Model and Cost Function
      • Parameter Learning
  • Week 2
    • Linear Regression with Multiple Variables
      • Multivariate Linear Regression
      • Computing Parameters Analytically
  • Week 3
    • Logistic Regression
      • Classification
      • Logistic Regression Model
      • Multiclass Classification
    • Regularization
      • Solving the Problem of Overfitting
  • Week 4
    • Neural Networks: Representation
      • Neural Networks
      • Applications
  • Week 5
    • Neural Networks: Learning
      • Cost Function and Backpropagation
      • Backpropagation in Practice
  • Week 6
    • Advice for Applying Machine Learning
      • Evaluating a Learning Algorithm
      • Bias vs. Variance
    • Machine Learning System Design
      • Building a Spam Classifier
      • Handling Skewed Data
      • Using Large Data Sets
  • Week 7
    • Support Vector Machines
      • Large Margin Classification
      • Kernels
      • SVMs in Practice
  • Week 8
    • Unsupervised Learning
      • Clustering
    • Dimensionality Reduction
      • Principal Component Analysis
      • Applying PCA
  • Week 9
    • Anomaly Detection
      • Density Estimation
      • Building an Anomaly Detection System
      • Multivariate Gaussian Distribution
    • Recommender Systems
      • Predicting Movie Ratings
      • Collaborative Filtering
      • Low Rank Matrix Factorization
  • Week 10
    • Large Scale Machine Learning
      • Gradient Descent with Large Datasets
      • Advanced Topics
  • Week 11
    • Application Example: Photo OCR
      • Photo OCR