Machine Learning

Notes from Coursera course

introduction

• 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

Cost Function

• Squared Error Function Or Mean Squared Error

$J(θ0​,θ1​)=2m1​i=1∑m​(y^​i​−yi​)2=2m1​i=1∑m​(hθ​(xi​)−yi​)2$