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Introduction to Machine Learning
Introduction
Varun ChandolaComputer Science & Engineering
State University of New York at BuffaloBuffalo, NY, USA
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Outline
Making Intelligent Machines
Human Learning
Definition of Machine LearningWhy Machine Learning?
Learning from (Past) Data
Overview of ML
Supervised Learning
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Making Machines Intelligent
The world is woven from billions of lives, every strand
crossing every other. What we call premonition is just
movement of the web. If you couldattenuate to every strand ofquivering data, the futurewould be entirely calculable, asinevitable as mathematics.
Sherlock Holmes, 2017
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What makes an artificial machine intelligent?
1. Talk. See. Hear.I Natural Language Processing, Computer Vision, Speech Recognition
2. Store. Access. Represent. (Knowledge)I Ontologies. Semantic Networks. Information Retrieval.
3. Reason.I Mathematical Logic. Bayesian Inference.
4. Learn.I Improve with Experience
I Machine Learning
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What makes an artificial machine intelligent?
1. Talk. See. Hear.I Natural Language Processing, Computer Vision, Speech Recognition
2. Store. Access. Represent. (Knowledge)I Ontologies. Semantic Networks. Information Retrieval.
3. Reason.I Mathematical Logic. Bayesian Inference.
4. Learn.I Improve with Experience
I Machine Learning
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Human Learning?
I What do we learn?I Concepts (this is a chair, that is not a chair)I Distinguishing concepts (this is a chair, that is a table)I Other things (language, juggling, using a remote)
I How do we learn?
1. Teaching (Passive).2. Experience (Active).
2.1 Examples.2.2 Queries.2.3 Experimentation.
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What is Machine Learning?
I Computers learn without being explicitly programmed.I Arthur Samuel (1959)
I A computer program learns from experience E with respect to sometask T, if its performance P while performing task T improves overE.I Tom Mitchell (1989)
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Why Machine Learning?
I Machines that know everything fromthe beginning?I Too bulky. Creator already knows
everything. Fails with newexperiences.
I Machines that learn?I Compact. Learn what is necessary.I Adapt.I Assumption: Future experiences are
not too different from pastexperiences.
I Have (structural) relationship.
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Learning from (Past) Data
Deductive LogicI All birds can fly
I Dodo is a bird
I ⇒ Dodo can fly
Inductive LogicI A stingray can swim
I Stingray is a fish
I ⇒ All fish can swim
Core TenetI Deduce Induce from past
I Generalize for future
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Learning from (Past) Data
Deductive LogicI All birds can fly
I Dodo is a bird
I ⇒ Dodo can fly
Inductive LogicI A stingray can swim
I Stingray is a fish
I ⇒ All fish can swim
Core TenetI Deduce Induce from past
I Generalize for future
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Understanding the Machine Learning Landscape
I What do you want the ML algorithm to do?
I How do you want to do it?
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Machine Learning Problems
Supervised LearningI Given a finite set
of x’s andcorresponding y ’s,learn f ()
I Infer y for a new x
I y - continuous(regression)
I y - discrete(classification)
UnsupervisedLearningI Given only x’s,
infer structure indataI hidden (latent)
relationshipsamong theobjects
I e.g., clustering,embedding,dimensionalityreduction, etc.
ReinforcementLearning*I Find the best
mapping ofsituations toactions tomaximize anumerical reward
I Agent learns tobehave in anenvironment
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Machine Learning meet Mother Nature
I Data: A collection of data objectsI A representation of the object of interest or
the state of the target system, and/orI A label or a target value or a low-dimensional
representation or an embedding or an actionassociated with the object/state
I Mother nature generates this data using anunknown generative process (secret recipe)
I ML problem - given part of the data, infer theother part
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Supervised Learning
https://quickdraw.withgoogle.com/
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More Examples
PrimaryI Given an individual’s data (criminal and otherwise), predict risk of
recidivism
I Given an email, determine if it is spam or normal
I Given an image, identify the object
I For a given day, predict the number of travelers that will passthrough a subway station
SecondaryI Color a black and white image
I Translate English text to French
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Unsupervised Learning
I Learn hidden structure in the data
I No assumption of labels
I Examples
1. Clustering (customers of Wegmans, set of magazine articles)2. Embedding any data into a metric space (<d) (text, tweets, disease
codes)3. Dimensionality reduction4. Factor analysis5. Dictionary learning
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Reinforcement Learning
I An agent, operating in a changing environment, finds the bestsequence of actions that maximize an end goal
I Key concepts: policy, reward, end-value, environment
I Examples
1. Learning to play a game (Breakout, AlphaGo)2. A robotic vaccum cleaner figures out the best time to recharge3. Almost all robotic tasks4. Traffic light control
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References
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