Introduction to Machine Learning

Introduction to
Machine Learning
Tiberio Caetano
NICTA and Australian National University

Friday, 24 February 2012

Quick calibration:


Quick calibration:

Who has heard of Machine Learning?


Quick calibration:


Who has used Machine Learning?


Quick calibration:


Who has used Machine Learning?

Who has built new Machine Learning tools?


PROBLEM:

DATA

ACTIONABLE KNOWLEDGE

That’s roughly the problem Machine Learning addresses


BLUE: DATA RED: KNOWLEDGE

- Is this email spam or not spam?

- Is there a face in this picture?

- Should I lend money to this customer given his
spending behaviour?


Knowledge is not concrete

“Face” is an abstraction
“Spam” is an abstraction
“Who to lend to” is an abstraction

You don’t find faces, spam or financial advice in datasets
you just find bits


?

We have data But we want abstractions


What is an abstraction
anyway?
• Anything whose description does not
depend exclusively on the bits you have

• Notion of generalisation is fundamental

• Abstraction always involves assumptions

Ready to deﬁne Machine Learning:

• Machine Learning is the science of
automating the process of abstraction from
raw data and assumptions

Raw Data
Machine Learning Abstraction
Assumptions


Data: (painted image) + (dataset of normal images)

+

Assumption: the non-painted parts of the painted image
behave as the images in the dataset


Data: (painted image) + (dataset of normal images)

+

Assumption: the non-painted parts of the painted image
behave as the images in the dataset

Abstraction: corrected image


Several forms of abstraction

Cluster data
Classify data
Predict from data
Summarise data
Decide based on data
etc...


(e) Ground Truth
Clustering

(i) Ground Truth

Figure 2: Resulting motion
based algorithms. 2(a)-2(d)

[10] S. M. Goldfeld and R
http://home.dei.polimi.it/matteucc/
Holland Publishing C
Clustering/tutorial_html/
[11] D. W. Hosmer. Maxim
lines. In Communicati

Dimensionality Reduction and Visualization

http://isomap.stanford.edu/datasets.html

Regression


Classiﬁcation

{spam; not spam} {0,1,2,3,4,5,6,7,8,9}


Structured Prediction

Image Understanding

Protein Structure
Prediction

Machine Translation

Image credit: S. Gould

Structured Prediction

Chess, NY, Kasparov, WTC

Kangaroo, Sun, Sea, Australia


What Machine Learning IS NOT



Find 01001000:



Find 01001000:

Machine Learning is not exact pattern matching



Find 01001000:


This is “just” classical computer science
classical “database query”, deduction



Find 01001000:


This is “just” classical computer science
classical “database query”, deduction
Machine Learning involves induction

But Machine Learning IS NOT classical statistics either


But Machine Learning IS NOT classical statistics either

- Complex rather than simple models
(forget Gaussianity, forget linearity)

- Numerical rather than analytical solution
(forget pencil-and-paper: need hardcore numerical optimization)

- VERY High rather than low dimensional
(p>>n rather than n>>p)


Some popular technologies driven by Machine Learning

Recommender Systems


Some popular technologies driven by Machine Learning

Social media


Big Data and Machine Learning

Parallelism is crucial

- Linear algebraic approaches favoured
(matrix multiplication-based)

- Much of Feature Extraction can be
parallelised

- Model Training is another story: usually
needs syncing


Machine Learning and Data Mining

Data Mining is a buzzword and in that sense it includes
Machine Learning

In a more strict sense, Data Mining is often associated to
data analysis without necessarily doing predictive analytics
(which is the hallmark of Machine Learning)


When is Machine Learning helpful?

DATA

When you don’t really know how to ﬁnd an explicit
(at the bit-level) description for your abstraction or
“actionable knowledge”


When is Machine Learning helpful?

DATA

When you don’t really know how to ﬁnd an explicit
(at the bit-level) description for your abstraction or
“actionable knowledge”

And this is common!!


http://tiberiocaetano.com

http://www.nicta.com.au/research/machine_learning


Introduction to Machine Learning

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