Notes from Coursera course


  • Supervised Learning: we have a data set and already know what our correct output should look like
    • Regression: Predict value on a curve
    • Classification: Predict Discrete value output, usually 1 or 0
  • Unsupervised Learning: Expected output unknown.
    • No feedback based on the prediction results
    • Derive relation/structure by grouping the data into clusters
    • e.g.
      • Market segmentation
      • Social network analysis.
    • Non Clustering : Find relation within chaotic environment
      • Cocktail Party problem
        • Separate out two voices, one closer to mic other away from mic.

Model representation

  • $X^{(i)}$ represents input data $Y^{(i)}$ represents output value
    • These are not exponentiation
  • A pair $(x^{(i)}, y^{(i)})$ is called a training example, and the dataset — a list of $m$ training examples $(x^{(i)},y^{(i)});i=1,...,m$ — is called a training set.
  • We need to find function h so that h(x) returns good predicted y
  • h is called hypothesis

Model Representation

Cost Function

  • Squared Error Function Or Mean Squared Error