stanford-machine-learning-course

Week 01

• Introduction
  • Wtf?
  • Supervised/ Unsupervised
• Linear regression with one variable
  • Model and Cost Function
    • Model representation
    • Cost function
  • Parameter Learning
    • Gradient descent
    • Gradient descent for linear regression
• Linear algebra review

Week 02

• Linear regression with multiple variables
  • Multivariate Linear Regression
  • Computing Parameters Analytically
• Octav/ Matlab usage

Week 03

• Logistic Regression
  • Classification and Representation
  • Logistic Regression Model
  • Multiclass Classification
• Regularization
  • Solving the Problem of Overfitting

Week 04

• Motivations
• Neural Networks
• Applications

Week 05

• Neural Networks: Learning
  • Cost Function and Backpropagation
  • Backpropagation in Practice
  • Application of Neural Networks

Week 06

• 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 07

• Support Vector Machines
  • Large Margin Classification
  • Kernels
  • SVMs in Practice

Week 08

• Unsupervised Learning
  • Clustering
  • Dimensionality Reduction

Week 09

• Anomaly Detection
  • Density Estimation
  • Building an Anomaly Detection System
  • Multivariate Gaussian Distribution (Optional)
• Recommender Systems
  • Predicting Movie Ratings
  • Collaborative Filtering
  • Low Rank Matrix Factorization

Week 10

• Gradient descent with large dataset
  • Learning with large dataset
  • Stochastic gradient descent
  • Mini-batch gradient descent
  • Stochastic gradient descent convergence
• Advanced topics
  • Online learning
  • MR

Week 11

• Photo OCR
  • Problem description
  • Sliding windows
  • Data and artificial data
  • Ceiling Analysis

You passed this course! Your grade is 93.30%.