basics of machine learning€¦ · basics of machine learning joel reijonen lead data scientist at...

Ericsson Internal | 2018-02-21

Basics of Machine Learning

Joel ReijonenLead Data Scientist at [email protected]


Machine Learning –Motivation

— We are living in a connected world where the connected devices and users produce more data than we have ever seen

— How do we cope with such high volumes of data?

— How do we ensure that services today will also function tomorrow?

— Here machine learning may become handy..

Figure taken from Ericsson Mobility Report: Internet of Things forecast


Machine Learning –What is it?

— Machine learning refers to the machine’s ability to improve its own performance independently

— Machine learning strives to extract additional, hidden, information from the data

— Machine learning performs well with multi-dimensional data whereas human gets easily lost in the details

Intelligence Amplification

Artificial Intelligence

Machine Learning

Machine Intelligence

Big Data Models

Algorithms


Rules

Machine Learning –New way of thinking

Algorithm Programming

Machine Learning

Rules

DataAnswers

Data

Answers


Functionality of Machine Learning

— In machine learning, the main idea is to construct a model

— The constructed model consists of parameters or weights

— Learning happens by updating weights

x1

x2

w11

w12

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w14

w21

w22

w23

y

Input layer

Hidden layer

Output layer

wij = weighted connection


Machine Learning Techniques

Unsupervised Learning

Supervised Learning

Machine Learning

Other Learning Techniques

Classification Regression Clustering Reinforcement Learning

Deep Learning


Supervised Learning

— Supervised learning is a technique where learning is based on training from examples or experiences in the past

— Training set is composed of labeled data which consists real observed input and output values.

— Goal: Predict class or value

Raw data

Supervisor

Supervised Learning

Training Outputs

Training inputs

Output


Supervised Learning –Classification

— Classification is a supervised learning method that determines for which output or class do the sampling of inputs belong to

— Classification can be used in applications such as image recognition, pattern detection and natural language processing

— Potential industrial use-cases: surveillance, quality control, system health monitoring

Class 2

Class 1

Classification rule


Supervised Learning –Regression

— Regression is a supervised learning method where the inputs of the system are mapped on outputs that are numeric values

— Regression is used in applications that take advantage of estimations, predictions and forecasting

— Potential industrial use-cases: battery power consumption, power plant engine load optimization, stock-market prediction

Regression



— Unsupervised learning is a technique that does not utilize the observed output values rather takes advantage of input values

— Unsupervised learning pursues to extract main features and structures of the data

— Goal: Determine groupings and pattern

Raw data


Output

Self-guided learning


Unsupervised Learning –Clustering

— Clustering is an unsupervised learning method where inputs with similar features and attributes are allocated in the same cluster

— Clustering can be utilized in applications such as image analysis, data mining and pattern recognition

— Potential industrial use-cases: customer clustering, fault detection in power plant engines

Cluster 1

Cluster 3

Cluster 2


Other Learning Techniques

— Other learning methods are techniques which may have similarities with supervised or unsupervised techniques but yet they have significant differences in their functionalities to be categorized differently

— Most of the other learning techniques take advantage of feature extraction and rewarding policies

— Goal: Learn features and achieve rewards from the environment

Raw data

Other Learning techniques

Output

Feature learning

Environment


Reward

Other Learning Techniques –Reinforcement Learning

— In reinforcement learning, the machine is rewarded if the actions or decisions that the machine has made have increased overall performance in a certain environment

— Reinforcement learning can be applied in applications that take advantage of multi-agent systems, controlling systems and swarm intelligence

— Potential industrial use-cases: traffic light management, robotics, video games

Action

Environment

Machine

State


Other Learning Techniques –Deep Learning

— Deep learning takes a new approach on learning multiple layers of meaningful representations

— Each learned layer introduces new extracted features

— Applications that take advantage of deep learning include, e.g., near-human-level image classification, speech recognition and natural language processing

— Potential industrial use-cases: robotics, advanced system management, digital assistants


Other Learning Techniques –Deep Learning

Fault detection

FailureNo Failure

Traditional Machine Learning approach

Input Feature extraction

Output

Fault detection

FailureNo Failure

Deep Learning approach

Input Feature extraction

Output


Neural Networks

— Neural networks are a common way to implement previously described machine learning techniques

— Neural networks are inspired by biological neural networks where high number of interconnected neurons process information in parallel

— In neural networks, the connections are called synapsesand processing units as neurons

— Goal: Neural networks strive to achieve human-like performance in wide field of applications

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Input layer

Hidden layer

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Output

Neural Networks –Training

Input

Neural Network

Target

Loss function

Optimizer

Modify weights

— Neural network training is iterative process where the learned performance of the neural network is evaluated by using a loss function

— Loss function (cost function) refers to an objective function that evaluates the performance of the neural network

— By utilizing results from loss function, the optimizer handles the weight modification by updating the weights in such a way that it strives to improve the performance of the neural network


Beyond Machine Learning –Artificial Intelligence

— In artificial intelligence, machine learning is an essential concept in order to enable continuous learning from examples or from experiences in the past

— Artificial intelligence take advantage of multiple functionalities but the two most obvious are learning and decisiong making

— In learning, artificial intelligence utilizes machine learning techniques to train models that describe the system behaviour and performance based on the training data

— In decision making, artificial intelligence utilizes trained machine learning models in inferences

— Based on these inferences, artificial intelligence can perform decisions by taking advantage of deduction logic

Machine Learning

Artificial Intelligence


Beyond Machine Learning –Data preparation

— In order to achieve reliable results in machine learning, raw data needs to be pre-processed

— Raw data may include noise, redundancy, missing values and domination that may cause counterproductive effects

— Proper data preparation is a preliminary requirement for efficient machine learning

Raw Data

Prepared Data

Structured Data

Cleaned Data


Beyond Machine Learning –Skills to Master

— In order to become a superstar in machine learning, I would recommend to learn following skills:

— Linear algebra

— Calculus

— Probability theory and statistics

— Theoretical computer science

— Programming skills e.g. in Python, R or C++

— As interesting machine learning frameworks, I would recommend following:

— TensorFlow

— PyTorch

— Scikit-learn

— Keras

— MXNet


Q & A

Thank you for participating!Contact: [email protected]

basics of machine learning€¦ · basics of machine learning joel reijonen lead data scientist at...

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