CETM53 Portfolio Exercise 4 : Screencast

CETM53 PORTFOLIO EXERCISE 4 (2021): SCREENCAST

By Diamond Francis

CONTENTS

1. Introduction

LIST OF FIGURES

2. Machine learning

Figure

3. Graph theory

Researcher’s Image......................5

4. Machine learning & graph theory

Region Map....................................5

5. Case study

SAGE Example..............................6

6. SAGE

METLAB Example........................7

7. METLAB 8. References

Chapter

1.INTRODUCTION

This presentation investigates into machine learning an area of computing.

Explores key points about the implementation of graph theory and machine learning practices.

2.MACHINE LEARNING

What is machine learning?

• Gets computers to learn and act like humans do • Training process that improves the learning overtime • Giving computers data in a form of observations • Giving computers data in a form of real world interactions

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data (Heath, 2020).

Machine learning has potential for growth expansion as this area of computing is persistently revolutionized.

3.GRAPH THEORY

What is graph theory?

• A graph is a structure made from a set of objects that are put together, making it an abstract concept • Graph theory maps out the relationships between these objects

Over the last two decades, graph theory has become increasingly popular in both research and industry (Sun, 2020).

Among other areas, it has been used in epidemiology, medicine genetics, healthcare, banking and engineering to solve challenges such as routing, finding relation, path etc.

(Sun, 2020).

4.MACHINE LEARNING & GRAPH THEORY How can graph theory be applied to machine learning?

• Can be applied to predict human behaviors • Improves reliability • Produces related results • Targets complex data

Just as graphs make it easier for us to understand and act on complex data, graph machine learning can take graph theory a giant step further (Sun, 2020).

5.CASE STUDY

Machine learning helps researchers categorize the ocean's ecology.

Scientists at MIT used machine learning to find distinct points enabled them to split the world’s oceans into different “provinces” based on ecological makeup (Pepalis, 2020).

Figure 1. Researcher’s Image (Pepalis, 2020)

5.CASE STUDY

Chlorophyll satellite maps gave scientists an idea of amount of life in a region.

The amount of phytoplankton’s green pigment in an area can show how productive one ecosystem might be compared to another (Pepalis, 2020).

Figure 2. Region Map (Chu, 2020)

The machine learning technique projects data from large complicated datasets into a simpler, lower-dimensional data set, which they named SAGE (Pepalis, 2020).

5.CASE STUDY

MIT used machine learning for the reason that: • Used Sage method to analyse the global ocean • Scientists would have the opportunity to use cutting edge computing software to achieve effective results • The data is too much for humans to sort • Heavily relied on machine learning to make the process easier, saving time

6.SAGE

Graph theory software is a useful tool to assist with the analysis of data.

• A mathematics software, Sage is a free open-source tool that packs impressive mathematical functionalities inside (Sharma, 2020) • Simple or outright concrete, Sage has a mathematical approach for creating graphs and is popular in the academic communities across the world (Sharma, 2020)

Figure 3. SAGE Example (Sharma, 2020)

7.MATLAB

MATLAB is a mathematical software application that is used for creating Graph theory.

• It has data visualization and exploration elements in addition to graph theory functions (Sharma, 2020) • MATLAB is really helpful when using graph theory for a large real-time project (Sharma, 2020) Figure 4. MATLAB Example (Sharma, 2020)

8.REFERENCES

Heath, N. (2020). What is Machine learning? Everything you need to know. [online]. Available at < https://www.zdnet.com/article/what-is-machine-learning-everything-you-need-to-know/> Accessed 02 Jan 21 Sun, W. (2020). What & why: Graph machine learning in distributed systems: What you need to know. [online].

Available at <https://www.ericsson.com/en/blog/2020/3/graph-machine-learning-distributed-systems> Accessed 02 Jan 21 Pepalis, B. (2020). Machine learning helps researchers categorize the ocean’s ecology. [online]. Available at [online]. < https://currentsciencedaily.com/stories/538959284-machine-learning-helps-researchers-categorize-the-ocean-s -ecology > Accessed 02 Jan 21 Chu, J. (2020). Machine learning helps map global ocean communities. [online]. Available at < https://news.mit.edu/2020/machine-learning-map-ocean-0529> Accessed 02 Jan 21 Sharma, A. (2020). Top 10 Graph Theory Software. [online]. Available at < https://analyticsindiamag.com/top-10-graph-theory-software/> Accessed 02 Jan 21

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