Machine Learning Lecture Notes

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分类专栏： Machine Learning Notes 文章标签：机器学习人工智能

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Machine Learning Lecture Notes-1[KCL Financial Mathematics]

1 Introduction

1 Introduction

1.1 Machine Learning v.s Statistics

Definition

Machine Learning : a field that takes an algorithmic approach to data analysis, processing and prediction.
Algorithmic :produces good predictions or extracts useful information from data to solve a practical problem

Venn diagram: Statictics\data science\AI

Figure 1 Venn Diagram

1.2 Applications

Supervised Learning

Definition:
Automate decision-making processes by generalising from input-output pairs $x_i , y_i )$ , $i \in$ { $1, . . . N$ } for some $\N$
Drawback
Creating a dataset of inputs and outputs is often a laborious manual process.
Advantage
Supervised learning algorithms are well-understood and their performance is easy to measure
Example(train tickets pricing by distance)

Unsupervised Learning

Definition
Only the input data is known, and no known output data is given to the algorithm.
Drawback
Harder to understand and evaluate than SL.
Advantage
Only the input data is needed and there is no process of “creating input-output pairs” involved.
Example(trade portfolio)

Data & Tasks & Algorithms

data(explain with case)
- data point
- feature
- feature extraction/engineering
task(figure: overview of ml tasks)
- regression
- classification
- clustering
- dimensonality reduction
< Different tasks have different loss functions, refer to 1.3–Function >
algorithm
- supportvectormachines(SVMs)
- nearestneighbours
- random forest
- k means
- matrix factorisaion/autoencoder
- …

1.3 Deep Learning

Definition(supervised & unsupervised)

Deep learning solves Problems by employing neural networks(artificial neural networks), functions constructed by composing alternatingly affine and (simple) non-linear functions.