AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised...

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AI and Machine Learning

Transcript of AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised...

Page 1: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

AI and Machine Learning

Page 2: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

The Turing Test

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Turing attack

How can we show a machine is Intelligent? Let A = machine. Let C = Intelligent. Let B = someone that “we” claim is intelligent. How can we show A = C? Hmm. It’s subjective? Well most (normal) say B = C. So if can we show that A = B, then we can show that A = C!

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Turing attack

So subjective criteria when normal can be reasoned with transitivity!E.g., let C = Greatest basketball player ever. Most “normal” say Jordan. Let Jordan = B. Now is Lebron = the greatest ever? Let Lebron = A. Well we have to show that A = C, or A = B. Let’s assume A = B. Do we wind up in a contradiction? If so A!=B. If not A = B and A = C. Is Lebron the greatest?

Page 5: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Chinese Room Argument

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Chinese Room ArgumentSearle defines:Strong AI = UnderstandingWeak AI = pattern recognition and mappings

What do you think?

Page 7: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Loebner prize MitsukaThe Loebner Prize is an annual competition in artificial intelligence that awards prizes to the computer programs considered by the judges to be the most human-like. The format of the competition is that of a standard Turing test. No one has ever won the silver or gold medal!

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Mitsuko

Loebnerbronze winner in 2013, 2016, 2017, 2018

Page 9: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Unsupervised vs. Supervised Learning

Supervised learning is aided by training data and human correction. Here’s some training data. Learn the patterns. Make your best guess at what the patterns are. We’ll feed you test data to figure out if you’ve understood it. If you stray of course we’ll correct you and retrain. Examples include Decision Trees and Neural Networks.

Unsupervised learning is uncorrected and runs on data. It can’t classify things “yet”. But is very good at clustering and anomaly detection.

Page 10: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Decision Tree Learning

• Input: an object or situation described by a set of attributes (or features)

• Output: a �decision� – the predicts output value for the input.

• The input attributes and the outputs can be discrete or continuous.

• We will focus on decision trees for Boolean classification: • each example is classified as positive or negative.

•

Task:– Given: collection of examples (x, f(x))– Return: a function h (hypothesis) that approximates f– h is a decision tree

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New York TimesApril 16, 2008

Can we learn how counties vote?

Decision Trees:a sequence of tests.Representation very natural for humans.Style of many �How to�manuals and trouble-shootingprocedures.

Page 12: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Decision Tree•What is a decision tree?

•A tree with two types of nodes:

• Decision nodes• Leaf nodes

••Decision node: Specifies a choice or test of some attribute with 2 or more alternatives;

•à every decision node is part of a path to a leaf node

•Leaf node: Indicates classification of an example

Page 13: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Inductive Learning ExampleFood

(3) Chat (2)

Fast (2)

Price (3)

Bar (2)

BigTip

great yes yes normal no yes great no yes normal no yes mediocre yes no high no no great yes yes normal yes yes

Instance Space X: Set of all possible objects described by attributes (often called features).

Target Function f: Mapping from Attributes to Target Feature (often called label) (f is unknown)

Hypothesis Space H: Set of all classification rules hi we allow.

Training Data D: Set of instances labeled with Target Feature

What is the best Variable (Feature) to use as an indicator of a BigTip?

Etc.

Page 14: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

Entropy & Information Gain

Obviously JDon’t Panic. Entropy is just a way to measure disorder = uncertainty in data and uncertainty in “data mappings”.

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Information Gain

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Excel example

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Decision Tree Example: “BigTip”

Food

Price

Speedyno

yes no

no

yes

great

mediocreyuck

yesno

adequatehigh

Is the decision tree we learned consistent?

Yes, it agrees with all the examples!

Our data

Data: Not all 2x2x3 = 12 tuplesAlso, some repeats! These areliterally “observations.”

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Top-Down Induction of Decision Tree:Big Tip Example

10 examples:

Food

y

gm

How many + and - examples per subclass, starting with y?

6+4-

1087431

2 5 6 9

6

1087431

2

5 9

No No

Let�s consider next the attribute Speedy

Speedy

yn

108731

4

2Yes Pricea h

4 2

Yes No

Node “done” when uniform label or “nofurtheruncertainty.”

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An example

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An example

Page 21: AI and Machine Learning - cs.umd.edu€¦ · Unsupervised vs. Supervised Learning Supervised learning is aided by training data and human correction. Here’s some training data.

An example

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Chapter 3: Supervised Learning · 2019. 11. 24. · Supervised vs. unsupervised Learning •Supervised learning: classification is seen as supervised learning from examples. •Supervision:

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Lecture 2: Supervised Learning | Classi cationjaven/talk/L2 Supervised Learning.pdf · Recap Lecture 1 Concepts of Supervised Learning (SL) Classi cation algorithms Supervised Learning

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Basic Technical Concepts in Machine Learning Introduction Supervised learning Problems in supervised learning Bayesian decision theory.

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Introduction to Machine Learning · Introduction to Machine Learning CMSC 422 MARINE CARPUAT marine@cs.umd.edu

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Chapter 5: Partially-Supervised Learning. CS583, Bing Liu, UIC 2 Outline Fully supervised learning (traditional classification) Partially (semi-) supervised.

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Nonparametric Supervised Learning - cda.psych.uiuc.educda.psych.uiuc.edu/multivariate_fall_2013/matlab_help/...learning.pdf · Supervised Learning (Machine Learning) Workflow and

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Bits of Machine Learning Part 1: Supervised Learning - …wasp-sweden.org/custom/...proutiere-supervised-learning-20161028.pdf · Bits of Machine Learning Part 1: Supervised Learning

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