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Lecture 1 Machine Learning Basics Sung Kim <[email protected]>

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Page 1: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Lecture 1 Machine Learning Basics

Sung Kim <[email protected]>

Page 2: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Basic concepts

• What is ML?

• What is learning?- supervised- unsupervised

• What is regression?

• What is classification?

Machine Learning

• Limitations of explicit programming- Spam filter: many rules- Automatic driving: too many rules

• Machine learning: "Field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel (1959)

Page 4: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Supervised/Unsupervised learning

• Supervised learning: - learning with labeled examples - training set

• Unsupervised learning: un-labeled data- Google news grouping

- Word clustering

Page 5: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Supervised learningAn example training set for four visual categories.

http://cs231n.github.io/classification/

Page 6: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Supervised/Unsupervised learning

• Supervised learning: - learning with labeled examples

• Unsupervised learning: un-labeled data- Google news grouping

- Word clustering

Page 7: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Supervised learning

• Most common problem type in ML - Image labeling: learning from tagged images

- Email spam filter: learning from labeled (spam or ham) email

- Predicting exam score: learning from previous exam score and time spent

Page 8: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Training data set

Page 9: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

AlphaGo

Page 10: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Types of supervised learning

• Predicting final exam score based on time spent - regression

• Pass/non-pass based on time spent- binary classification

• Letter grade (A, B, C, E and F) based on time spent - multi-label classification

Page 11: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Predicting final exam score based on time spent

x (hours) y (score)

10 90

9 80

3 50

2 30

Page 12: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Pass/non-pass based on time spent

x (hours) y (pass/fail)

10 P

9 P

3 F

2 F

Page 13: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Letter grade (A, B, …) based on time spent

x (hours) y (grade)

10 A

9 B

3 D

2 F

Page 14: ML with Tensorflowhunkim.github.io/ml/lec1.pdf · Machine Learning •Limitations of explicit programming Spam ﬁlter: many rules-Automatic driving: too many rules •Machine learning:

Linear regression

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