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INTRODUCTION TO MACHINE LEARNING
Machine Learning: What’s The Challenge?
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Introduction to Machine Learning
Goals of the course
● Identify a machine learning problem
● Use basic machine learning techniques
● Think about your data/results
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Introduction to Machine Learning
What is Machine Learning?
● Construct/use algorithms that learn from data
● More information Higher performance
● Previous solutions Experience
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Introduction to Machine Learning
Example
● Task for computer = label unseen square:
?
● Label squares: size and edge color
● Earlier observations (labeled by humans):
● Result: right or wrong!
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Introduction to Machine Learning
Input KnowledgeIn example: pre-labeled squares
size edge color
small do!ed green
big striped yellow
medium normal green
Observations
Features Label
> squares <- data.frame( size = c("small", "big", "medium"), edge = c("dotted", "striped", "normal"), color = c("green", "yellow", "green"))
In R - use data.frame()
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Introduction to Machine Learning
Data Frame Functions
> str(squares)
> summary(squares)
> dim(squares) #Observations, #Features
Distribution Measures
Structured Overview
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Introduction to Machine Learning
Formulation
INPUT FUNCTION OUTPUT
ESTIMATED FUNCTION COLOR
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Introduction to Machine Learning
ML: What It Is Not
NOT Machine Learning}
Goal: Building models for prediction!
● Determining most occurring color
● Calculating average size
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Introduction to Machine Learning
Regression
INPUT: WeightOUTPUT: Height
Regression
Height Weight
Estimated function:
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Introduction to Machine Learning
More Applications!● Shopping basket analysis
● Movie recommendation systems
● Decision making for self-driving cars
● and many more!
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INTRODUCTION TO MACHINE LEARNING
Let’s practice!
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INTRODUCTION TO MACHINE LEARNING
Classification Regression Clustering
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Introduction to Machine Learning
Common ML Problems
● Classification
● Regression
● Clustering
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Introduction to Machine Learning
Classification Problem
EstimateCLASSIFIER
ClassUnseen DataCLASSIFIER
Goal: predict category of new observation
Earlier Observations
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Introduction to Machine Learning
Classification Applications● Medical Diagnosis
● Animal Recognition
● Qualitative Output
● Predefined Classes
Important:
Sick and Not Sick
Dog, Cat and Horse
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Introduction to Machine Learning
Regression
PREDICTORSREGRESSION
FUNCTION RESPONSE
● Relationship: Height - Weight?
● Linear?
● Predict: Weight Height
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Introduction to Machine Learning
Regression Model
● Coefficients:
Estimate on previous input-output
> lm(response ~ predictor)
Fi"ing a linear function
● Response:
● Predictor:
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Introduction to Machine Learning
Regression Applications● Payments Credit Scores
● Time Subscriptions
● Grades Landing a Job
● Quantitative Output
● Previous input-output observations
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Introduction to Machine Learning
Clustering
● Similar within cluster
● Dissimilar between clusters
● No labels
● No right or wrong
● Plenty possible clusterings
● Clustering: grouping objects in clusters
● Example: Grouping similar animal photos
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Introduction to Machine Learning
k-Means
0 5 10
−50
5
x
y
Cluster data in k clusters!
0 5 10
−50
5
x
y
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INTRODUCTION TO MACHINE LEARNING
Let’s Practice
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INTRODUCTION TO MACHINE LEARNING
Supervised vs.
Unsupervised
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Introduction to Machine Learning
Machine Learning Tasks● Classification
● Regression
● Clustering
quite similar
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Introduction to Machine Learning
Supervised LearningFind: function f ̂which can be used to assign a
class or value to unseen observations.
Given: a set of labeled observations
Supervised Learning
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Introduction to Machine Learning
Unsupervised Learning● Labeling can be tedious, o!en done by humans
● Some techniques don’t require labeled data
● Unsupervised Learning
● Clustering: find groups observation that are similar
● Does not require labeled observations
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Introduction to Machine Learning
Performance of the model● Supervised Learning
● Compare real labels with predicted labels
● Predictions should be similar to real labels
● Unsupervised Learning
● No real labels to compare
● Techniques will be explained in this course
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Introduction to Machine Learning
Semi-Supervised Learning● A lot of unlabeled observations
● A few labeled
● Group similar observations using clustering
● Use clustering information and classes of labeled observations to assign a class to unlabelled observations
● More labeled observations for supervised learning
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INTRODUCTION TO MACHINE LEARNING
Let’s practice!