A brief lesson on what constitutes computational decision making, from simple regression via various classification methods to deep learning. No maths, only basic concepts to teach the lingo of machine learning to a lay audience.

1. Computational
decision making
Dr. Boris Adryan
@BorisAdryan

2. What I aim to
provide
✓basic vocabulary for
✓fundamental concepts of
computational decision making
✓phenomenological introduction
to machine learning methods
✓a rough idea when and how to
use these methods

3. What this
presentation isn’t
x hands-on tutorial
x thorough summary
x comprehensive guide
x technical deep dive
x statistics course

4. Is this artificial
intelligence?
word = input(‘Enter a word:’)
for key in British_dictionary.iteritems():
if key.startswith(word):
print(‘This is a British word.’)

5. Is this machine
learning?
temperature = float(input('What is the temperature?'))
if temperature >= 1.0:
print('Wear shorts.')
else:
print('Wear long underwear.’)

6. Definition
Rule-based decision making on
the basis of numeric thresholds
or string patterns etc is not
machine learning.
And most definitely it is
not artificial intelligence.

7. But what if…
…the threshold is inferred at
run time?
“Write a software that says how
close to Euston you can move if
you can afford to spend £650k.”
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Average property price
Northern Line, number of stops from Euston
table = input_table(‘cost at station’)
print(where_x_yields_a_low_enough_y)

8. Linear regression
is probably the most simple
“machine learning” method.
Average property price
Northern Line, number of stops from Euston
It is an example of supervised
learning, because we teach the
computer the relation between
an input variable (“feature”) and
an output variable (“label”).
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0 3 6 9 12
y = m . x + b

9. Linear regression
can become arbitrarily
complicated.
The difference between curve fitting
in statistics and machine learning is
mostly semantics.
f(number of stops to Euston,
square footage, bedrooms,
bathrooms, …) price
many features
Euston
High
Barnet
small
large
price

10. Classification tasks I. setosa
I. virginica
I. versicolor
All images from https://en.wikipedia.org/wiki/Iris_flower_data_set
Rather than projecting the feature
vector onto a continuous variable,
many supervised learning
methods identify “class labels”.

11. Classification tasks
Supervised learning requires
complete input matrices. Missing
or nonsensical values have to be
replaced or removed.
Non-numerical features (think, e.g.
“name of colour”, “smell”) have to
be encoded.
class
label
feature 1 feature 2 feature 3 feature 4
1 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 7.0 3.2 4.7 1.4
3 6.3 3.3 6.0 2.5

12. Classification tasks
sepal length
sepalwidth
I. setosa
I. virginica
In a first approximation,
classification (by regression) aims
to find a function that best
separates the different class labels.
f(sepal width, sepal length) (1,2)
“1”
“2”

13. sepal length
sepalwidth
I. setosa
I. virginica
I. versicolor
Decision trees
sepal width
petalwidth
A decision tree can be understood
as a series of linear separations of
the data.
ratio of sepal width : sepal length
I. virginica
ratio of petal width : sepal width
I. setosa I. versicolor

14. Random forests
A collection of decision trees, each
trained on a random subset of the
data, can minimise the risk of
over-fitting.
A single big decision tree trained
on all data can effectively describe
a single data point.
sepal width
petalwidth

15. Over-/Underfitting
Sloppy separation is called
underfitting, and greedy
separation overfitting.
Counteracting an overfit is called
regularisation, and works by
penalising too many features (L1)
or too strong feature weights (L2).
underfit (high bias)
okay
overfit (high variance)

16. Dimensionality
reduction
Dimensionality reduction aims to
reduce the complexity of a dataset
(in respect to number of features).
The first principal components are
dimensions that explain most of a
dataset’s variance.

17. Support vector
machine
The SVM aims to provide an ideal
separation plane by supporting it
with a training data vector.
A classification margin protects the
SVM against overfitting.
?
margin support vectors
decision boundary
wTx = 0
negative hyperplane
wTx = -1
positive hyperplane
wTx = 1

18. Kernel trick
Input data can be projected into a
higher-dimensional space that
allows linear separation of
otherwise inseparable data.
There are different kernels, such
as the radial basis function
(Gaussian) or polynomial kernel.
“2D” input space
Φ
“3D” feature space
http://scikit-learn.org/0.18/auto_examples/svm/plot_iris.html

19. features
weather forecast
airport location
# of gates
# of runways
# of
snowploughs
airline
aircraft
BLACK
BOX
training
flights
cancelled in
the past classifier
ranked list of
relevant features
weight of
features
thresholds for
features
performance
metric
prediction
new data
General
approach
An intuitive example from real life.

20. training
classifier
performance
assessment
good enough?
success!
moredatafortraining
data
no
yes
sensitivity
“truepositives”
1-specificity
“false positives”
0 0.2 0.4 0.6 0.8 1.0
1.0
0.8
0.6
0.4
0.2
worse than
random
guess
Classifier
performance
Not all machine learning behaves
ideal, and performance metrics
are important for quality checks
and parameter tuning.

21. https://en.wikipedia.org/wiki/Precision_and_recall
There is a wide range of performance
metrics, comprising combinations of
true & false positives as well as
true & false negatives.
Metrics zoo
positive class (P) negative class (N)
predicted positive true positive (TP) false positive (FP)
predicted negative false negative (FN) true negative (TN)

22. data acquisition model building test use in production
data recording
(production system)
evaluation
raw data clean-up feature engineering model learning model selection
labour intense compute intensebrain intense
development
production
ML pipeline

23. Choosing a method
from: Olson et al., 2017, https://arxiv.org/abs/1708.05070
There is no ‘one-size-fits-all’
machine learning method. Most
methods need to be carefully
tuned to perform ideal.
Often, there a ‘non-functional’
constraints on choosing a method.
Runtime, interpretability, etc.

24. What about
neural networks?
feature 1 feature 2 feature 3
weight 1 weight 2 weight 3
input function
activation function
class output
error for weight updates
a simple perceptron
Neural networks attempt to mimic
the integrative properties of neurons.
The perceptron is a single-layer
network.
inputs
outputs

25. Deep neural networks
http://www.asimovinstitute.org/neural-network-zoo
While many artificial neural networks
show great performance, the basis on
which features exactly the classification
works remains largely unknown.

26. In RL, the methods iteratively
learn to optimise an output from
an abstract representation of a
system
Reinforcement
learning
move (l/r)
or shoot
unknown
systemmap (210 x 160 pixels, 8-bit RGB)
actual score
choose action on basis
of map to optimise score
Mnih et al., Nature (2015)

27. Machine learning can help to
structure and explore an
unknown dataset.
These methods aid the
identification of classes where
their existence isn’t known yet.
Unsupervised
learning
• Hierarchical clustering
• K-means clustering
• Expectation maximisation
• Density-based clustering
plus
clever visualisation

28. Hierarchical clustering
Derives hierarchical dependencies of
individual rows and columns in the
dataset on the basis of similarity
(correlation) between their properties.
Combined with a heatmap, gives a good
first impression of a dataset.

29. k-means clustering
Defines k different centroids to which
data points are assigned by proximity. If
the distance doesn’t get much smaller, k
is the number of clusters in the set.
Density-based clustering and expectation
maximisation are conceptually related,
the latter giving a probability for
membership in any group.
k = 2
k vs centroid distance

30. Conclusions
https://badryan.github.io/2015/10/20/is-it-all-machine-learning.html
•People on the Internet steal
infographics.
•ML methods have been around in
the stats world for ages, but big
data sets and compute power
make them more widely known.
•Understanding key principles
behind ML should be part of the
school curriculum.