CS534 Machine Learning - Classes

CS534 Machine Learning
Spring 2013
Lecture 1:
Introduction to ML
Course logistics
Reading:
The discipline of Machine learning by Tom Mitchell

Course Information
• Instructor: Dr. Xiaoli Fern
Kec 3073, xfern@eecs.oregonstate.edu
• TA: Travis Moore
• Office hour (tentative)
Instructor: MW before class 11‐12 or by appointment
TA: TBA (see class webpage for update)
• Class Web Page
classes.engr.oregonstate.edu/eecs/spring2013/cs534/
• Class email list
cs534‐sp13@engr.orst.edu

Course materials
• Text book:
– Pattern recognition and machine learning by Chris
Bishop (Bishop)
• Slides and reading materials will be provided on
course webpage
• Other good references
– Machine learning by Tom Mitchell (TM)
– Pattern Classification by Duda, Hart and Stork (DHS)
2 nd edition
• A lot of online resources on machine learning
– Check class website for a few links
3

Prerequisites
Color Green
means important
• Basic probability theory and statistics
concepts: Distributions, Densities,
Expectation, Variance, parameter estimation
– A brief review is provided on class website
• Multivariable Calculus and linear algebra
– Basic review slides, and links to useful video
lectures provided on class webpage
• Knowledge of basic CS concepts such as data
structure, search strategies, complexity
Please spend some time review these!
It will be tremendously helpful!

Homework Policies
• Homework is generally due at the beginning of
the class on the due day
• Each student has one allowance of handing in
late homework (no more than 48 hours late)
• Collaboration policy
– Discussions are allowed, but copying of solution or
code is not
– See the Student Conduct page on OSU website for
information regarding academic dishonesty
(http://oregonstate.edu/studentconduct/code/ind
ex.php#acdis)

Grading policy
• Grading policy:
Written homework will not be graded based on correctness. We will
record the number of problems that were "completed" (either
correctly or incorrectly).
Completing a problems requires a non‐trivial attempt at solving the
problem. The judgment of whether a problem was "completed" is
left to the instructor and the TA.
• Final grades breakdown:
– Midterm 25%; Final 25%; Final project 25%; Implementation
assignments 25%.
– The resulting letter grade will be decreased by one if a student fails
to complete at least 80% of the written homework problems.

What is Machine learning
Task T
Performance P
Learning Algorithm
Experience E
Machine learning studies algorithms that
• Improve performance P
• at some task T
• based on experience E

Machine learning in Computer Science
• Machine learning is already the preferred approach to
– Speech recognition, Natural language processing
– Computer vision
– Medical outcomes analysis
– Robot control
– …
• This trend is growing
– Improved machine learning algorithms
– Increase data capture, and new sensors
– Increasing demand for self‐customization to user and
environment

Fields of Study
Machine Learning
Supervised
Learning
Semi‐supervised
learning
Unsupervised
Learning
Reinforcement
Learning

Supervised Learning
• Learn to predict output from input.
• Output can be
– continuous: regression problems
$
x
x
x
x x x
x
x
x
x x
x
x
Example: Predicting the
price of a house based on
its square footage
x
feet

Supervised Learning
• Learn to predict output from input.
• Output can be
– continuous: regression problems
– Discrete: classification problems
Example: classify a loan
applicant as either high
risk or low risk based on
income and saving
amount.

Unsupervised Learning
• Given a collection of examples (objects),
discover self‐similar groups within the data –
clustering
Example: clustering
artwork

Unsupervised learning
• Given a collection of examples (objects),
discover self‐similar groups within the data –
clustering
Image Segmentation
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Unsupervised Learning
• Given a collection of examples (objects),
discover self‐similar groups within the data –
clustering
• Learn the underlying distribution that
generates the data we observe – density
estimation
• Represent high dimensional data using a lowdimensional
representation for compression
or visualization – dimension reduction

Reinforcement Learning
• Learn to act
• An agent
– Observes the environment
– Takes action
– With each action, receives rewards/punishments
– Goal: learn a policy that optimizes rewards
• No examples of optimal outputs are given
• Not covered in this class. Take 533 if you want
to learn about this.

When do we need computer to learn?

Appropriate Applications for
Supervised Learning
• Situations where there is no human expert
– x: bond graph of a new molecule, f(x): predicted binding strength to AIDS
protease molecule
– x: nano modification structure to a Fuel cell, f(x): predicted power output
strength by the fuel cell
• Situations where humans can perform the task but can’t describe
how they do it
– x: picture of a hand‐written character, f(x): ascii code of the character
– x: recording of a bird song, f(x): species of the bird
• Situations where the desired function is changing frequently
– x: description of stock prices and trades for last 10 days, f(x): recommended
stock transactions
• Situations where each user needs a customized function f
– x: incoming email message, f(x): importance score for presenting to the user
(or deleting without presenting)
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Supervised learning
• Given: a set of training examples
, ,
– : the input of the ‐th example ( i.e., a
vector)
– is its corresponding output (continuous or discrete)
– We assume there is some underlying function that
maps from to –our target function
• Goal: find a good approximation of so that
accurate prediction can be made for previously
unseen

The underline function:

Polynomial curve fitting
• There are infinite functions that will fit the training data perfectly.
• In order to learn, we have to focus on a limited set of possible
functions
– We call this our hypothesis space
– E.g., all M‐th order polynomial functions
2
y( x,
w)
w w x w x ...
0
1
M
w M x
– w = (w 0 , w 1 ,…, w M ) represents the unknown parameters that we
wish to learn from the training data
• Learning here means to find a good set of parameters
w to minimize some loss function
2
This optimization problem can be solved easily.
We will not focus on solving this at this point, will revisit this later.

Important Issue: Model Selection
• The red line shows the function learned with different M values
• Which M should we choose –this is a model selection problem
• Can we use E(w) that we define in previous slides as a criterion to
choose M?

Over‐fitting
• As M increases, loss on the training data
decreases monotonically
• However, the loss on test data starts to
increase after a while
• Why? Is this a fluke or generally true?
It turns out this is
generally the case –
caused by over‐fitting

Over‐fitting
• Over‐fitting refers to the phenomenon when the
learner adjusts to very specific random features
of the training data, which differs from the target
function
• Real example:
– In Bug ID project, x: image of a robotically
maneuvered bug, f(x): the species of the bug
– Initial attempt yields close to perfect accuracy
– Reason: the different species were imaged in different
batches, one species when imaging, has a peculiar air
bubble in the image.

Overfitting
• Over‐fitting happens when
– There is too little data (or some systematic bias in
the data )
– There are too many parameters

Key Issues in Machine Learning
• What are good hypothesis spaces?
– Linear functions? Polynomials?
– which spaces have been useful in practical applications?
• How to select among different hypothesis spaces?
– The Model selection problem
– Trade‐off between over‐fitting and under‐fitting
• How can we optimize accuracy on future data points?
– This is often called the Generalization Error –error on unseen data pts
– Related to the issue of “overfitting”, i.e., the model fitting to the peculiarities
rather than the generalities of the data
• What level of confidence should we have in the results? (A
statistical question)
– How much training data is required to find an accurate hypotheses with high
probability? This is the topic of learning theory
• Are some learning problems computationally intractable? (A
computational question)
– Some learning problems are provably hard
– Heuristic / greedy approaches are often used when this is the case
• How can we formulate application problems as machine learning
problems? (the engineering question)
25

Terminology
• Training example an example of the form
– x: feature vector
– y
• continuous value for regression problems
• class label, in [1, 2, …, K] , for classification problems
• Training Set a set of training examples drawn randomly from
P(x,y)
• Target function the true mapping from x to y
• Hypothesis: a proposed function h considered by the learning
algorithm to be similar to the target function.
• Test Set a set of training examples used to evaluate a
proposed hypothesis h.
• Hypothesis space The space of all hypotheses that can, in
principle, be output by a particular learning algorithm
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