Machine Learning - Introduction to Artificial Intelligence - Lecture Slides, Slides of Artificial Intelligence

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CSE 151 Intro. to AI: A Statistical

Approach

CSE 151 Machine Learning

What is Machine Learning?

How to use data to learn to make better predictions Example 2: Spam Detection

What is Machine Learning?

How to use data to learn to make better predictions Example 3: Link Prediction

Two Types of Machine Learning

Supervised Learning Given examples of data and their labels, predict labels of new (unseen) data Unsupervised Learning Given data, build a model or cluster There are other types, but we won’t get to it in this class

Supervised Learning

Given labeled data: Classification:

(xi, yi)

where y is discrete , find a rule to predict y values for unseen x feature vector label i=1,..,n

Typical Classification Algorithm

Set of input examples (xi, yi) Classification Algorithm Prediction Rule New example x Label y Training Data Test Data Training and test data must be separate!

Typical Classification Algorithm

Set of input examples (xi, yi) Classification Algorithm Prediction Rule New example x Label y Training Data Test Data Performance Measure: Accuracy (or fraction of correct answers) on test data

Supervised Learning

Classification: Given labeled data (xi, yi) where y is discrete , predict y values for unseen x Example 1: Predict if a patient has flu or not Yes (^) No 99F Yes (^1 0 99) + Fever Cold Temperature Flu?

x y

Label: Flu/No flu Features: Properties of patient A binary (two-label) classification problem

Supervised Learning

Classification: Given labeled data (xi, yi) where y is discrete , predict y values for unseen x Example 2: Which digit in the image? A multiclass classification problem Label: 0,1,.., What are the features?

Supervised Learning

Classification: Given labeled data (xi, yi) where y is discrete , predict y values for unseen x Example 2: Which digit in the image? Lesson: Choosing features is non-trivial in real applications Label: 0,1,.., What are the features? Option: vector of pixel colors There are other options too

Supervised Learning

Classification: Given labeled data (xi, yi) where y is discrete , predict y values for unseen x Example 3: Spam or not? Label: 0 (not spam), 1 (spam) Features: Words in the email Subject: Offer ends now! From: Canadian Pharmacy Subject: TA meeting From:Yuncong Chen Pharmacy Email1 Email 2 offer meeting (^) TA Spam? Email 1 1 1 0 0 Yes Email 2 0 0 1 1 No

Supervised Learning

Regression: Given data (xi, yi) where y is continuous , predict y values for unseen x Example 1: Predict house price from properties of house Bedrooms (^) Bathrooms

x y

Dependent variable: price Independent Variable: Property of house Area Price (^3 2 2000) 600K (^2 1 1200) 400K

Two Types of Machine Learning

Supervised Learning Given examples of data and their labels, predict labels of new (unseen) data Unsupervised Learning Given data, build a model There are other types, but we won’t get to it in this class Examples: Classification, Regression