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
- 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.
- 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
h is called
- Squared Error Function Or Mean Squared Error