Machine Learning Basics and Supervised, unsupervised

Unit 1 Introduction to Machine learning
1
Lecture
Number
Topic Hours
1 Classic and adaptive machines, Machine learning
matters
01
2 Beyond machine learning-deep learning 01
3 Bio inspired adaptive systems 01
4 Machine learning and Big data 01
5 Important Elements of Machine Learning- Data
formats, Learnability
01
6 Statistical learning approaches 01
7 Elements of information theory 01
Total hours =07

Introduction to Machine Learning
2

Life before Machine Learning
3

Classic and adaptive machines
• machine is never efficient or trendy without a
concrete possibility to use it with pragmatism.
• A machine is immediately considered useful
and destined to be continuously improved if
its users can easily understand what tasks can
be completed with less effort or completely
automatically

Generic representation of a classical
system

Machine learning matters
• learning is the ability to change according to
external stimuli and remembering most of all
previous experiences
• machine learning is an engineering approach
that gives maximum importance to every
technique that increases or improves the
propensity for changing adaptively.

Goal of ML
• to study, engineer, and improve mathematical
models which can be trained (once or
continuously) with context-related data
(provided by a generic environment), to infer
the future and to make decisions without
complete knowledge of all influencing
elements (external factors).

Goal of ML
• In other words
• an agent (which is a software entity that receives
information from an environment, picks the best
action to reach a specific goal, and observes the
results of it) adopts a statistical learning
approach, trying to determine the right
probability distributions and use them to
compute the action (value or decision) that is
most likely to be successful (with the least error).

Supervised learning
• A supervised scenario is characterized by the concept
of a teacher or supervisor, whose main task is to
provide the agent with a precise measure of its error
(directly comparable with output values).
• In a supervised scenario, the goal is training a system
that must also work with samples never seen before.
So, it's necessary to allow the model to develop a
generalization ability and avoid a common problem
called overfitting, which causes an overlearning due to
an excessive capacity

Common supervised learning applications
include:
• Predictive analysis based on regression or
categorical classification
• Spam detection
• Pattern detection
• Natural Language Processing
• Sentiment analysis
• Automatic image classification
• Automatic sequence processing (for example,
music or speech)

Unsupervised learning
• This approach is based on the absence of any
supervisor and therefore of absolute error
measures; it's useful when it's necessary to
learn how a set of elements can be
grouped(clustered) according to their
similarity (or distance measure)

Commons unsupervised applications
include
• Object segmentation (for example, users,
products, movies, songs, and so on)
• Similarity detection
• Automatic labeling

Reinforcement Learning
• reinforcement learning is also based on feedback
provided by the environment
• the information is more qualitative and doesn't
help the agent in determining a precise measure
of its error. In reinforcement learning, this
feedback is usually called reward (sometimes, a
negative one is defined as a penalty) and it's
useful to understand whether a certain action
performed in a state is positive or not.

Reinforcement Learning
• The sequence of most useful actions is a policy that the
agent has to learn, so to be able to make always the
best decision in terms of the highest immediate and
cumulative reward. In other words, an action can also
be imperfect, but in terms of a global policy it has to
offer the highest total reward.
• Reinforcement learning is particularly efficient when
the environment is not completely deterministic, when
it's often very dynamic, and when it's impossible to
have a precise error measure.

Reinforcement Learning: Example
22

Machine Learning algorithms
23

Reinforcement Learning: Example
27

Beyond machine learning
deep learning , bio inspired adaptive
systems
• complex (deep) neural architectures: deep learning
– Rosenblatt invented the first perceptron, interest in neural
networks
• The idea behind these techniques is to create
algorithms that work like a brain
• Common deep learning applications include:
– Image classification
– Real-time visual tracking
– Autonomous car driving
– Logistic optimization
– Bioinformatics
– Speech recognition

Machine learning and Big data.
• the amount of information managed in different
business contexts grew exponentially
• opportunity to use it for machine learning
purposes arose
• it's possible to asynchronously train several local
models, periodically share the updates, and re-
synchronize them all with a master model
• Not every machine learning problem is suitable
for big data, and not all big datasets are really
useful when training models.

Parametric learning
• a vector-values function depend on an
internal parameter vector which determines
the actual instance of a generic predictor, the
approach is called parametric learning:

non-parametric learning
• doesn't make initial assumptions about the
family of predictors
• instance-based learning and makes real-time
predictions (without pre-computing
parameter values) based on hypothesis
determined only by the training samples
(instance set).
• concept of neighborhoods :classification

• In a classification problem, a new sample is
automatically surrounded by classified
training elements and the output class is
determined considering the preponderant one
in the neighborhood
• Eg: kernel-based support vector machines

• A generic parametric training process must
find the best parameter vector which
minimizes the regression/classification error
given a specific training dataset and it should
also generate a predictor that can correctly
generalize when unknown samples are
provided.

• Supervised Learning
– Regression and classification
• Single value /single label
• Multi value: multi-label classification and multi-output
regression
• Unsupervised learning
– We only have an input set X with m-length
vectors, and we define clustering function (with n
target clusters) with the following expression:

IMPORTANT ELEMENTS IN MACHINE
LEARNING

two possibilities
• If we expect future data to be exactly distributed as
training samples, a more complex model can be a good
choice, to capture small variations that a lowerlevel
one will discard. In this case, a linear (or lower-level)
model will drive to underfitting, because it won't be
able to capture an appropriate level of expressivity.
• If we think that future data can be locally distributed
differently but keeps a global trend, it's preferable to
have a higher residual misclassification error as well as
a more precise generalization ability. Using a bigger
model focusing only on training data can drive to
overfitting.

Underfitting and overfitting
• The purpose of a machine learning model is to approximate an
unknown function that associates input elements to output ones
(classes)
• Underfitting: It means that the model isn't able to capture the
dynamics shown by the same training set (probably because its
capacity is too limited).
• Overfitting: the model has an excessive capacity and it's not more
able to generalize considering the original dynamics provided by the
training set. It can associate almost perfectly all the known samples
to the corresponding output values, but when an unknown input is
presented, the corresponding prediction error can be very high.

• low-capacity (underfitting) :
– easier to detect considering the prediction error
• normal-capacity (normal fitting):
– may prove to be more difficult to discover as it could
be initially considered the result of a perfect fitting.
• excessive capacity (overfitting):

Data formats
• In a supervised learning problem, there will
always be a dataset, defined as a finite set of
real vectors with m features each:
Considering approach is always probabilistic each X as
drawn from a statistical multivariate distribution D.

• dataset X: we expect all samples to be independent and
identically distributed (i.i.d).
• This means all variables belong to the same distribution D,
and considering an arbitrary subset of m values, it happens
that:
• The corresponding output values can be both numerical-
continuous or categorical.
• In the first case, the process is called regression, while in the
second, it is called classification.

Numerical Outputs
Categorical examples are

• Generic Regressor : a vector-valued function
which associates an input value to a
continuous output
• Generic Classifier, a vector-values function
whose predicted output is categorical
(discrete).

Multiclass strategies
• number of output classes is greater than one
• two main possibilities to manage a
classification problem:
– One-vs-all
– One-vs-one
• the choice is transparent and the output
returned to the user will always be the final
value or class.

One-vs-one
• training a model for each pair of classes
• complexity is O(n2)
• the right class is determined by a majority
vote.
• this choice is more expensive and should be
adopted only when a full dataset comparison
is not preferable.

One-vs-all
• widely adopted by scikit-learn
• If there are n output classes, n classifiers will
be trained in parallel considering there is
always a separation between an actual class
and the remaining ones.
• lightweight approach (O(n) complexity)

Learnability
• A parametric model can be split into two
parts: a static structure and a dynamic set of
parameters.
• a static structure : is determined by choice of
a specific algorithm and is normally
immutable
• dynamic set of parameters is the objective of
our optimization

Learnability
• Considering n unbounded parameters, they
generate an n-dimensional space where each
point, together with the immutable part of the
estimator function, represents a learning
hypothesis H
• The goal of a parametric learning process is to
find the best hypothesis whose corresponding
prediction error is minimum and the residual
generalization ability is enough to avoid
overfitting.

• the dataset X is linearly separable (without
transformations) if there exists a hyperplane
which divides the space into two subspaces
containing only elements belonging to the
same class

• Cross-validation and other techniques easily
show how our model works with test samples
never seen during the training phase.
• generic rule of thumb says that a residual error is
always necessary to guarantee a good
generalization ability, while a model that shows a
validation accuracy of 99.999... percent on
training samples is almost surely overfitted and
will likely be unable to predict correctly when
never-seen input samples are provided.

Error measures
• In general, when working with a supervised
scenario, we define a non-negative error
measure em which takes two arguments
(expected and predicted output) and allows us
to compute a total error value over the whole
dataset (made up of n samples):

loss function
• it's useful to consider the mean square error (MSE):
• A generic training algorithm has to find the global
minimum or a point quite close to it (there's always a
tolerance to avoid an excessive number of iterations
and a consequent risk of overfitting).
• This measure is also called loss function because its
value must be minimized through an optimization
problem.

zero-one-loss
• efficient for binary classifications (also for one-
vs-rest multiclass strategy):
• adopted in loss functions based on the
probability of misclassification

Statistical learning approaches
• Imagine that you need to design a spam-filtering algorithm
starting from this initial (over simplistic) classification based
on two parameters:
• We have collected 200 email messages (X) : p1 and p2
mutually exclusive
• find a couple of probabilistic hypotheses to determine

Machine Learning Basics and Supervised, unsupervised

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