Machine Learning

1. Introduction

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Machine learning

Programs with parameters that automatically adjust by adapting to previously seen data.

  • Machine learning can be considered a subfield of artificial intelligence...
  • ...since those algorithms can be seen as building blocks to make computers learn to behave more intelligently.
  • Generalize instead of that just storing and retrieving data items like a database system would do.

Machine learning: A modern alchemy

  • Data is more abundant -and least expensive- than knowledge.
  • Professionals from various areas of industry work on a particular philosopher's stone:

Turn data into knowledge!

Work in machine learning:
Alchemic treatise of [Ramon Llull](

Intelligent systems find patterns and discover relations that are latent in large volumes of data.

Features of intelligent systems:

  • Learning
  • Adaptation
  • Flexibility and robustness
  • Provide explanations
  • Discovery/creativity


Learning is the act of acquiring new, or modifying and reinforcing, existing knowledge, behaviors, skills, values, or preferences and may involve synthesizing different types of information.

  • Construction and study of systems that can learn from data.


  • The environment/real world is in constant change.
  • The capacity to adapt implies to be able to modify what has been learn in order to cope with those modifications.
  • There are many real-world cases:
    • Changes in economy
    • Wear of mechanic parts of a robot
  • In many instances the capacity to adapt is essential to solve the problem $\rightarrow$ continuous learning.

Flexibility and robustness

  • It is required to have a robust and consistent system.
    • Similar inputs should generate consistent outputs.
  • Self-organization
  • 'Classical' approaches based on Boolean algebra and logic have limited flexibility.


  • Explanations are necessary to validate and find directions for improvement.
  • It is not enough to automate the decision making process.
    • In many context explanations are necessary: medicine, credit evaluation, etc.
  • They are important if a human expert takes part of the decission loop.
  • Machine learning can become a research tool.


  • Capacity of discovering processes and/or relations previously unknown.
  • Creation of solution and artifacts.

Example: Evolving cars with genetic algorithms:

More formally, the machine learning can be described as:

  • Having a process $\vec{F}:\mathcal{D}\rightarrow\mathcal{I}$ that transforms a given $\vec{x}\in\mathcal{D}$ in a $\vec{y}$.
  • Construct on a dataset $\Psi=\left\{\left<\vec{x}_i,\vec{y}_i\right>\right\}$ with $i=1,\ldots,N$.
  • Each $\left<\vec{x}_i,\vec{y}_i\right>$ represents an input and its corresponding expected output: $\vec{y}_i=\vec{F}\left(\vec{x}_i\right)$.
  • Optimize a model $\mathcal{M}(\vec{x};\vec{\theta})$ by adjusting its parameters $\vec{\theta}$.
    • Make $\mathcal{M}()$ to be as similar as possible to $\vec{F}()$ by optimizing one or more error (loss) functions.

Note: Generally, $\mathcal{D}\subseteq\mathbb{R}^n$; the definition of $\mathcal{I}$ depends on the problem.

Classes of machine learning problems

  • Classification: $\vec{F}: \mathcal{D}\rightarrow\left\{1,\ldots, k\right\}$; $\vec{F}(\cdot)$ defines 'categories' or 'classes' labels.
  • Regression: $\vec{F}: \mathbb{R}^n\rightarrow\mathbb{R}$; it is necessary to predict a real-valued output instead of categories.
  • Density estimation: predicit a function $p_\mathrm{model}: \mathbb{R}^n\rightarrow\mathbb{R}$, where $p_\mathrm{model}(\vec{x})$ can be interpreted as a probability density function on the set that the examples were drawn from.
  • Clustering: group a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups (clusters).
  • Synthesis: generate new examples that are similar to those in the training data.

Many more: times-series analysis, anomaly detection, imputation, transcription, etc.

Supervised learning

  • Sometimes we can observe the pairs $\left<\vec{x}_i,\vec{y}_i\right>$:
    • We can use the $\vec{y}_i$'s to provide a scalar feedback on how good is the model $\mathcal{M}(\vec{x};\vec{\theta})$.
    • That feed back is known as the loss function.
    • Modify parameters $\vec{\theta}$ as to improve $\mathcal{M}(\vec{x};\vec{\theta})$ $\rightarrow$ learning.

An example of a supervised problem (regression)

In [1]:
import random
import numpy as np
import matplotlib.pyplot as plt
In [2]:
# plt.rc('text', usetex=True); plt.rc('font', family='serif')
# plt.rc('text.latex', preamble='\\usepackage{libertine}\n\\usepackage[utf8]{inputenc}')

# numpy - pretty matrix 
np.set_printoptions(precision=3, threshold=1000, edgeitems=5, linewidth=80, suppress=True)

import seaborn

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
In [3]:
# Fixed seed to make the results replicable - remove in real life!
In [4]:
x = np.arange(100)

Let's suppose that we have a phenomenon such that $$y_\text{real} = \sin\left(\frac{\pi x}{50}\right)\,.$$

In [5]:
y_real = np.sin(x*np.pi/50)

Introducing some uniform random noise to simulate measurement noise:

In [6]:
y_measured = y_real + (np.random.rand(100) - 0.5)/1
In [7]:
plt.scatter(x,y_measured, marker='.', color='b', label='measured')
plt.plot(x,y_real, color='r', label='real')
plt.xlabel('x'); plt.ylabel('y'); plt.legend(frameon=True);

We can now learn from the dataset $\Psi=\left\{x, y_\text{measured}\right\}$.

Training (adjusting) SVR

In [8]:
from sklearn.svm import SVR
In [9]:
clf = SVR() # using default parameters, 1), y_measured)
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

We can now see how our SVR models the data.

In [10]:
y_pred = clf.predict(x.reshape(-1, 1))
In [11]:
plt.scatter(x, y_measured, marker='.', color='blue', label='measured')
plt.plot(x, y_pred, 'g--', label='predicted')
plt.xlabel('X'); plt.ylabel('y'); plt.legend(frameon=True);

We observe for the first time an important negative phenomenon: overfitting.

We will be dedicating part of the course to the methods that we have for control overfitting.

In [12]:
clf = SVR(C=1e3, gamma=0.0001), 1), y_measured)
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.0001,
  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
In [13]:
y_pred_ok = clf.predict(x.reshape(-1, 1))
In [14]:
plt.scatter(x, y_measured, marker='.', color='b', label='measured')
plt.plot(x, y_pred, 'g--', label='overfitted')
plt.plot(x, y_pred_ok, 'm-', label='not overfitted')
plt.xlabel('X'); plt.ylabel('y'); plt.legend(frameon=True);

What if... we don't have a label?

Unsupervised learning

In some cases we can just observe a series of items or values, e.g., $\Psi=\left\{\vec{x}_i\right\}$:

  • It is necessary to find the hidden structure of unlabeled data.
  • We need a measure of correctness of the model that does not requires an expected outcome.
  • Although, at first glance, it may look a bit awkward, this type of problem is very common.

    • Related to anomaly detection, clustering, etc.

An unsupervised learning example: Clustering

Let's generate a dataset that is composed by three groups or clusters of elements, $\vec{x}\in\mathbb{R}^2$.

In [15]:
x_1 = np.random.randn(30,2) + (5,5)
x_2 = np.random.randn(30,2) + (10,0)
x_3 = np.random.randn(30,2) + (0,2)
In [16]:
plt.scatter(x_1[:,0], x_1[:,1], c='red', label='Cluster 1', alpha =0.74)
plt.scatter(x_2[:,0], x_2[:,1], c='blue', label='Cluster 2', alpha =0.74)
plt.scatter(x_3[:,0], x_3[:,1], c='green', label='Cluster 3', alpha =0.74)
plt.legend(frameon=True); plt.xlabel('$x_1$'); plt.ylabel('$x_2$'); 
plt.title('Three datasets');

Preparing the training dataset.

In [17]:
x = np.concatenate(( x_1, x_2, x_3), axis=0)
(90, 2)
In [18]:
plt.scatter(x[:,0], x[:,1], c='m', alpha =0.74)
plt.title('Training dataset');

We can now try to learn what clusters are in the dataset. We are going to use the $k$-means clustering algorithm.

In [19]:
from sklearn.cluster import KMeans
In [20]:
clus = KMeans(n_clusters=3)
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
    random_state=None, tol=0.0001, verbose=0)
In [21]:
labels_pred = clus.predict(x)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 0 2 2 2]
In [22]:
for label in np.unique(labels_pred):
    plt.scatter(x[labels_pred==label][:,0], x[labels_pred==label][:,1],
                c=next(cm), alpha =0.74, label='Pred. cluster ' +str(label+1))
plt.legend(loc='upper right', bbox_to_anchor=(1.45,1), frameon=True);
plt.xlabel('$x_1$'); plt.ylabel('$x_2$'); plt.title('Clusters predicted');

Needing to set the number of clusters can lead to problems.

In [23]:
clus = KMeans(n_clusters=10)
labels_pred = clus.predict(x)
In [24]:
for label in np.unique(labels_pred):
    plt.scatter(x[labels_pred==label][:,0], x[labels_pred==label][:,1],
                c=next(cm), alpha =0.74, label='Pred. cluster ' + str(label+1))
plt.legend(loc='upper right', bbox_to_anchor=(1.45,1), frameon=True)
plt.xlabel('$x_1$'); plt.ylabel('$x_2$'); plt.title('Ten clusters predicted');

Semi-supervised learning:

  • Obtaining a supervised learning dataset can be expensive.
  • Some times it can be complemented with a "cheaper" unsupervised learning dataset.
  • What if we first learn as much as possible from unlabeled data and then use the labeled dataset.

Reinforcement learning

  • Inspired by behaviorist psychology;
  • How to take actions in an environment so as to maximize some notion of cumulative reward?
  • Differs from standard supervised learning in that correct input/output pairs are never presented,
  • ...nor sub-optimal actions explicitly corrected.
  • Involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

Problems types vs learning

Problems types and learning

Components of a machine learning problem/solution

  • A parametrized family of functions $\mathcal{M}(\vec{x};\theta)$ describing how the learner will behave on new examples.
    • What output $\mathcal{M}(\vec{x};\theta)$ will produce given some input $\vec{x}$?
  • A loss function $\ell()$ describing what scalar loss $\ell(\hat{\vec{y}}, \vec{y})$ is associated with each supervised example $\langle x, y\rangle$, as a function of the learner's output $\hat{\vec{y}} = \mathcal{M}(\vec{x};\theta)$ and the target output $\vec{y}$.
  • Training consists in choosing the parameters $\theta$ given some training examples $\Psi=\left\{\left<\vec{x}_i,\vec{y}_i\right>\right\}$ sampled from an unknown data generating distribution $P(X, Y)$.

Components of a machine learning problem/solution (II)

  • Define a training criterion.

    • Ideally: to minimize the expected loss sampled from the unknown data generating distribution.
    • This is not possible because the expectation makes use of the true underlying $P()$...
    • ...but we only have access to a finite number of training examples, $\Psi$.
    • A training criterion usually includes an empirical average of the loss over the training set,

      $$\min_{\theta}\ \mathbf{E}_{\Psi}[\ell(\mathcal{M}(\vec{x};\vec{\theta}), \vec{y})].$$

Components of a machine learning problem/solution (III)

  • Some additional terms (called regularizers) can be added to enforce preferences over the choices of $\vec{\theta}$.
$$\text{min}\ \mathbf{E}_{\Psi}[\ell(f_\theta(\vec{x}), \vec{y})] + R_{1}(\theta)+\cdots+R_{r}(\theta).$$
  • An optimization procedure to approximately minimize the training criterion by modifying $\theta$.

Datasets and evaluation

  • It is clear now that we need a dataset for training (of fitting or optimizing) the model.
    • Training dataset
  • We need another dataset to assess progress and compute the training criterion.
    • Testing dataset
  • As most ML approaches are stochastic and to contrast different approaches we need to have another dataset.
    • Validation dataset

This is a cornerstone issue of machine learning and we will be comming back to it.

The machine learning flowchart

from Scikit-learn [Choosing the right estimator](

Nature-inspired machine learning

  • Cellular automata
  • Neural computation
  • Evolutionary computation
  • Swarm intelligence
  • Artificial immune systems
  • Membrane computing
  • Amorphous computing

Final remarks

  • Different classes of machine learning problems:
    • Classification
    • Regression
    • Clustering.
  • Different classes of learning scenarions:
    • Supervised,
    • unsupervised,
    • semi-supervised, and
    • reinforcement learning.
  • Model, dataset, loss function, optimization.


Creative Commons License
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](
In [25]:
%load_ext version_information
%version_information scipy, numpy, matplotlib
Python3.6.2 64bit [GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]
OSDarwin 17.0.0 x86_64 i386 64bit
Tue Aug 22 09:03:13 2017 -03
In [26]:
# this code is here for cosmetic reasons
from IPython.core.display import HTML
from urllib.request import urlopen