# 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

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