# Introduction to Machine Learning

# What is Machine Learning?

Learning:the process of improve automatically with experience →Machine Learning: a computer program learn fromexperiencewith respect tosome tasks, andperformance measure.

**Three components:**

**Tasks**: e.g. classification, pattern recognition…**Performance measure:**e.g. accuracy and error… represented as matrices**Experience**: e.g. training dataset with known class labels

# Design Learning System

most ML learning process follows this procedures

**1. Choose training experience (E)**

how well can training experience represents the test examples (when the system has to make the final decisions i.e. support business decisions)

most machine learning theories

**assume**that training examples are identical to the distribution of the test examples, but this is rarely the reality

**2. Choose representation of target function**

we need to know how to represent what we want to achieve from the end results, representation of target function is closely tied with the algorithm choice

there is a tradeoff between

**expressibility**and the amount of**training data required**more expressive representation means that more training data required to choose among alternative hypothesis

*e.g. simply said, linear algebra y = ax + b can be treated as a target function (which is extended to linear regression in ML), other functions can be artificial neural network (ANN) …*

**3. Choose learning algorithm**

similar to how human learn from previous experience, target function algorithm needs constant updates while being trained, the goal is to

**minimize the error**two examples of learning algorithm:

*minimizing sum of squared errors (SSE) is used in Regression**least mean squared (LMS) is used in Artificial Neural Network (ANN)*

# Learning as Search

Learning can be seen as the process ofsearch through hypothesis spaceand find the best hypothesis that is consistent with the training dataset.

**What is the hypothesis space?**

choice of values for weights in the linear regression

can be represented as finding values to fill in matrices

**How to search?**

relate to concepts in Artificial Intelligence Local Search, it is the process of finding the optimal value among all hypotheses

*e.g. use hill-climbing searching to find local optima**e.g. ANN use genetic algorithm which is a kind of local beam search*

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