Chapter 1 - Introduction

1. Applications and problems

Applications

Text or document classification, e.g., spam detection;

Natural language processing, e.g., morphological analysis, part-of-speech tagging, statistical parsing, named-entity recognition;

Speechrecognition, speech synthesis, speaker verification;

Optical character recognition (OCR);

Computational biology applications, e.g., protein function or structured prediction;

Computer vision tasks, e.g., image recognition, face detection;

Faud detection (credit card, telephone) and network intrusion;

Games, e.g., chess, backgammon;

Medical diagnosis;

Recommendation systems, search enginesm information extraction systems.

Problems

Classification

Regression

Ranking

Clustering

Dimensionality reduction or manifold learning

1.2 Definitions and terminology

Examples

Features

Labels

Training sample

Validation sample

Test sample

Loss function

Hypothesis set

1.3 Cross-validation

In practice, the amount of labeled data available is often too small to set aside a validation sample since that would leave an insufficient amount of training data. Instead, a widely adopted method known as n-fold cross-validation is used to exploit the labeled data both for model selection (selection of the free parameters of the algorithm) and for training.

1.4 Learning scenarios

Supervised learning

The learner receives a set of labeled examples as training data and makes predictions for all unseen points.

Unsupervised learning

The learner exclusively receives unlabeled training data,and makes predictions for all unseen points.

Semi-unsupervised learning

The learner receives a training sample consisting of both labeled and unlabeled data, and makes predictions for all unseen points.

Transductive inference

As in the semi-supervised scenario, the learner receives a labeled training sample along with a set of unlabeled test points. However, the objective of transductive inference is to predict labels only for these particular test points.

On-line learning

In contrast with the previous scenarios, the online scenario
involves multiple rounds and training and testing phases are intermixed. At each

round, the learner receives an unlabeled training point, makes a prediction, receives

the true label, and incurs a loss

Reinforcement learning

The training and testing phases are also intermixed in reinforcement learning. To collect information, the learner actively interacts with the environment and in some cases affects the environment, and receives an immediate reward for each action. The object of the learner is to maximize his reward over a course of actions and iterations with the environment.

Active learning

　　The learner adaptively or interactively collects training examples,
typically by querying an oracle to request labels for new points.