Anomaly Detection

Problem Motivation

Just like in other learning problems, we are given a dataset x⁽¹⁾,x⁽²⁾,…,x^(m)

We are then given a new example, x_test, and we want to know whether this new example is abnormal/anomalous.

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Clustering

Unsupervised Learning: Introduction

Unsupervised learning is contrasted from supervised learning because it uses an unlabeled training set rather than a labeled one.

In other words, we don’t have the vector y of expected results, we only have a dataset of features where we can find structure.

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SVM and Kernels

Optimization Objective

Ps:
向量机（Vector Machine）：简称VM。向量算机的简称。

The Support Vector Machine (SVM) is yet another type of supervised machine learning algorithm. It is sometimes cleaner and more powerful.

Recall that in logistic regression, we use the following rules:

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Advice for Applying Machine Learning

Deciding What to Try Next

Errors in your predictions can be troubleshooted by:

• Getting more training examples
• Trying smaller sets of features
• Trying additional features
• Trying polynomial features
• Increasing or decreasing λ

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Neural Networks

Cost Function

Let’s first define a few variables that we will need to use:

• L = total number of layers in the network
• s_l = number of units (not counting bias unit) in layer l
• K = number of output units/classes

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Neural Networks

Model Representation I

Let’s examine how we will represent a hypothesis function using neural networks. At a very simple level, neurons are basically computational units that take inputs (dendrites) as electrical inputs (called “spikes”) that are channeled to outputs (axons). In our model, our dendrites are like the input features x₁…x_n and the output is the result of our hypothesis function. In this model our x₀ input node is sometimes called the “bias unit.” It is always equal to 1. In neural networks, we use the same logistic function as in classification, 1/(1+e^ΘTx),yet we sometimes call it a sigmoid (logistic) activation function. In this situation, our “theta” parameters are sometimes called “weights”.

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