CS194-10 Fall 2011 Introduction to Machine

Course outline
Overview of machine learning (today)
Classical supervised learning
Linear regression,
perceptrons,
neural nets,
SVMs,
decision trees,
nearest neighbors
Learning probabilistic models
Probabilistic classifiers (logistic regression, etc.)
Unsupervised learning, density estimation, EM
Bayes net learning
Time series models
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning is….
… a computational process for improving performance based on experience
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning: Why?
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning: Why?
The baby, rushed by eyes, ears, nose, skin, and insides at once, feels it all as one great blooming, buzzing confusion …
[William James, 1890]
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning: Why?
The baby, assailed by eyes, ears, nose, skin, and entrails at once, feels it all as one great blooming, buzzing confusion …
[William James, 1890]
Learning is essential for unknown environments,
i.e., when the designer lacks knowledge
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning: Why?
Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child brain is something like a notebook as one buys it from the stationer's. Rather little mechanism, and lots of blank sheets.
[Alan Turing, 1950]
Learning is useful as a system construction method, i.e., expose the system to reality rather than trying to write it down
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Structure of a learning agent
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Design of learning element
Key questions:
What is the agent design that will implement the desired performance?
Improve the performance of what piece of the agent system and how is that piece represented?
What data are available relevant to that piece? (In particular, do we know the right answers?)
What knowledge is already available?
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Examples
Supervised learning: correct answers for each training instance
Reinforcement learning: reward sequence, no correct answers
Unsupervised learning: “just make sense of the data”
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Supervised learning
To learn an unknown target function f
Input: a training set of labeled examples (xj,yj) where yj = f(xj)
E.g., xj is an image, f(xj) is the label “giraffe”
E.g., xj is a seismic signal, f(xj) is the label “explosion”
Output: hypothesis h that is “close” to f, i.e., predicts well on unseen examples (“test set”)
Many possible hypothesis families for h
Linear models, logistic regression, neural networks, decision trees, examples (nearest-neighbor), grammars, kernelized separators, etc etc
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Supervised learning
To learn an unknown target function f
Input: a training set of labeled examples (xj,yj) where yj = f(xj)
E.g., xj is an image, f(xj) is the label “giraffe”
E.g., xj is a seismic signal, f(xj) is the label “explosion”
Output: hypothesis h that is “close” to f, i.e., predicts well on unseen examples (“test set”)
Many possible hypothesis families for h
Linear models, logistic regression, neural networks, decision trees, examples (nearest-neighbor), grammars, kernelized separators, etc etc
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Supervised learning
To learn an unknown target function f
Input: a training set of labeled examples (xj,yj) where yj = f(xj)
E.g., xj is an image, f(xj) is the label “giraffe”
E.g., xj is a seismic signal, f(xj) is the label “explosion”
Output: hypothesis h that is “close” to f, i.e., predicts well on unseen examples (“test set”)
Many possible hypothesis families for h
Linear models, logistic regression, neural networks, decision trees, examples (nearest-neighbor), grammars, kernelized separators, etc etc
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Example: object recognition
x
f(x)
giraffe
giraffe
giraffe
llama
llama
llama
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Example: object recognition
x
f(x)
giraffe
giraffe
giraffe
llama
llama
llama
X=
f(x)=?
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Example: curve fitting
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Example: curve fitting
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Example: curve fitting
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Example: curve fitting
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning
data
Learning
knowledge
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning
data
Learning
knowledge
prior
knowledge
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell

Learning
data
Learning
knowledge
prior
knowledge
Lecture 1 8/25/11
CS 194-10 Fall 2011, Stuart Russell