# Learning From Data Note 1

## The Learning Problem

### The essence of machine learning (three components)

• A pattern exists
• We cannot pin it down mathematically
• We have data on it

### Formalization

• Intput: x

• Output: y

• Target function: f : X -> Y

• Data: (x1 y1), (x2, y2), ... , (xn, yn) (historical records)

• Hypothesis: g : X -> Y (final hypothesis is the trained model, g is close to f)

### Learning Model

learning model = hypothesis set + learning algorithm

### How does it work

learning-paradigm.png

### Perceptron Learning Algorithm (PLA)

• The perception implements: h(X) = sign(w * x)
• Given the training data: (x1, y1), (x2, y2), ... , (xn, yn)
• pick a misclassified point: sign(W * Xn) != yn
• and update the weight vector: w <- w + xn * yn (yn = (-1, 1), so w <- w +/- xn)

### Types of learning

#### Basic premise of learning

"using a set of observations to uncover an underlying process" -- broad premise that be applied in many disciplines

• Supervised learning: See above
• Unsupervised learning: Instead of (input, correct output), we get (input, ?). Cluster is one of them.
• Reinforcement learning: Instead of (input, correct output), we get (input, some output, grade for this output). It does not give you the output directly, but when you choose an output, it will tell you how well you're doing. It is interesting because it is mostly our own experience in learning. The most important application of reinforcement learning is in playing games. (like Alpha Go?)

### A Learning puzzle

The target function is unknown, it could be anything.
We have a finite sample, it can be anything outside.

### Finally

I'm new in machine learning and my English is not good enough. So please talk to me if there are any mistakes.

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