Mac hine L e ar ning Intr oduc tion Stanley Liang, PhD York - - PDF document

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6/2/2017 Mac hine L e ar ning Intr oduc tion Stanley Liang, PhD York University What is Mac hine L e ar ning? Definitio n The goal of machine learning is to program computers to use example data or past experience to solve a given

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Mac hine L e ar ning Intr

duc tion

Stanley Liang, PhD York University

Definitio n

“The goal of machine learning is to program computers to use example data or

past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.”

– ACM (Association for Computing Machinery)

“How do we create computer programs that improve with experience”

– Tom Mitchell

“Machine learning is the science of getting computers to act without being

explicitly programed.”

– Andrew Ng

What is Mac hine L e ar ning?

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T ypic al Mac hine L e ar ning topic s

Supervised Learning Unsupervised Learning Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make

predictions. The training dataset includes input data and

response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting

f input data without labeled responses.

Classification / Regression Clustering, semi‐supervised learning

Classification: for categorical response values, where the

data can be separated into specific “classes”.

Regression: for continuous-response values.
Clustering: the method for exploratory data

analysis to find hidden patterns or grouping in

data. The clusters are modeled using a measure of

similarity which is defined upon metrics such as Euclidean or probabilistic distance.

Semi‐supervised learning is a class of supervised

learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Supervised

learning is to build a model that makes predictions based on evidence in the presence of uncertainty.

As adaptive algorithms identify patterns in data, a computer

ʺlearnsʺ from the observations. When exposed to more

bservations,

the computer improves its predictive performance.

Typical Algorithms

– Classification: Decision Trees, Discriminant Analysis, Naive Bayes, k Nearest Neighbors (kNN), Support Vector Machines (SVM), Classification Ensembles ( a predictive model composed of a weighted combination of multiple classification models) – Regression: Linear Regression, Generalized Linear Models, Nonlinear Regression, Support Vector Machines (SVM), Gaussian Process Regression Models, Decision Trees, Regression Tree Ensembles

Supe r vise d L e ar ning Algor ithms

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Unsupervised learning is a type of machine learning algorithm

used to draw inferences from datasets consisting of input data without labeled responses.

Typical Algorithms

– Hierarchical clustering: builds a multilevel hierarchy of clusters by creating a cluster tree – k‐Means clustering: partitions data into k distinct clusters based on distance to the centroid of a cluster – Gaussian mixture models: models clusters as a mixture of multivariate normal density components – Self‐organizing maps: uses neural networks that learn the topology and distribution of the data – Hidden Markov models: uses observed data to recover the sequence of states

Unsupervised learning methods are used in bioinformatics for

sequence analysis and genetic clustering; in data mining for sequence and pattern mining; in medical imaging for image segmentation; and in computer vision for object recognition

Unsupe r vise d L e ar ning Algor ithms Mac hine L e ar ning F lowc har t

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Mac hine L e ar ning Apps

Classification Learner App Regression Learner App

Underfitting occurs when a statistical

model or machine learning algorithm cannot capture the underlying trend

f the data.
Underfitting would occur when fitting

a linear model to non‐linear data. Such a model would have poor predictive performance.

Overfitting occurs when a model is

excessively complex, such as having too many parameters relative to the number of observations.

Overfitting model has poor predictive

performance, as it overreacts to minor fluctuations in the training data.

Unde r

fitting vs. Ove r
fitting

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Cross validation: a model validation

technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used to estimate how accurately a predictive model will perform in practice.

Confusion matrix: a specific table

layout that allows visualization of the performance of an algorithm. Each column of the matrix represents the instances in a predicted class while each row represents the instances in an actual class.