代做FIN3020S Introduction to Machine Learning 2024代写C/C++程序

BBS 39 FT

FIN3020S

Introduction to Machine Learning

Copyright April 2024

WELCOME MESSAGE

I am delighted to introduce you to the Introduction to Machine Learning module.

This module is a management-level introduction to the topic of machine learning informed alert models, with a focus on the deployment and accountability of these models to mitigate operational risks and other fundamental risks in banking and finance. Operational risk machine learning informed alert models are deployed, for the most part, to detect, prevent and even predict suspicious and possibly fraudulent transactions. Instances include credit card fraud, elder financial exploitation and money laundering. These alert models are also deployed to mitigate or prevent cyber security crime and to inform. credit extension decisions. You will learn why these types of risks are centrally important in modern banking and how to monitor, mitigate and manage these risks.

The module will show students how to make sense of impactful, vast and complex data sets, i.e. “Big Data”, with a view to better understanding and anticipating operational risks that have emerged in the field of banking. Vast data sets are of no value to an organisation without algorithmic work to elicit meaning from the data. Machine learning will be introduced via the software RapidMiner. The alert models and monitoring skills introduced in this module are in high demand in industry. The module’s content is important not only for those students who wish to progress their careers in the financial services industry. It is also designed for those planning to pursue work in other industry sectors, and who wish to avail of and develop their quantitative risk management skill sets to mitigate a variety of risks.

On successful completion of this module, you will have achieved two outcomes: (1) developed a critical understanding of machine learning informed alert models in a banking context and (2) have learned how to evaluate the performance, ethics and reliability of these machine learning informed alert models.

This module will require your available free time for study over the coming weeks.  I will do all that I can to help and support you. The success of a student’s performance in this course will depend, to a large extent, on his or her time and effort devoted to the course materials. It is highly recommended that students maintain a consistent routine of studies and complete the course assignments at the earliest opportunity. Many management decisions are based on quantitative analysis and students should endeavor to understand the meaning behind the numbers, which in turn assist in the making of financial decisions and mitigating risks.

Please read this study guide carefully.  You should find the answer to any question you might have about the course in this document.   However, should you require any clarification on any matter pertaining to this module, don’t hesitate to contact me.

I am looking forward to meeting you.

PART 1:  INTRODUCTION

This Study Guide is designed to provide you with details of this module; the learning outcomes; plus, delivery and assessment arrangements. The Study Guide consists of 6 parts.

Part 1 gives background details to the subject area and the broad aims of the module are set out.

Part 2 consists of the module outline. In this part the (a) module learning outcomes, (b) the themes and topics to be explored are explained along with the (c) learning supports to be used.

Part 3 gives details of the module delivery arrangements. It sets out the session arrangements and the expectations in relation to your prior preparation and student engagement.

Part 4 provides details of the assessment techniques used in this module explaining the assessment components, their rationale.

Part 5 explains the UCD grading policy and grade descriptors drawing on the university document are given for each assessment component for the module.

Part 6 provides extended details of the themes and topics covered in this module, including weblinks and references.

Background Details

Introduction to Machine Learning is an increasingly important part of any syllabus of a contemporary finance degree. This module seeks to provide broad theoretical and practical knowledge of the principles, tools and methodologies used by financial managers at financial institutions from the machine learning ‘toolbox’ today. Further, the module seeks to develop a student’s ability to critically assess, evaluate, and analyse financial and operational risks.  The knowledge that students gain in this course leads to careers in the finance and treasury departments of a corporation or in risk management in banking.  Advance preparation for classes and workshops will be an important feature of this module, with readings, references and a slide deck for reflection assigned to students prior to the commencement of lectures.

Module Aims

The aim of this module is to provide students with an overview of the theory and practice of how a financial institution can manage risks.

On successful completion of this module, you will have achieved two outcomes: (1) developed a critical understanding of machine learning informed alert models in a banking context and (2) have learned how to evaluate the performance, ethics and reliability of these machine learning informed alert models.

Participants will be expected to contribute critical reflection of their experiences along with collaborative interpretation of such experiences in both classroom and study group settings. The module draws on student prior learning and work experience and combines insights from basic mathematics, accounting, economics, statistics, and other areas. The assessment tasks for this module have been designed with this in mind as detailed later in the study guide. Many of the quantitative aspects covered in this module can be completed using RapidMiner.

Programme Goals

Table 1 – Programme Goals Table

Programme Code: FIN3020S

Programme Title: BBS

Pathway: Finance

PART 2:  MODULE OUTLINE

Module Title:  Introduction to Machine Learning

Module Code: FIN3020S

No. of ECTS: 10.0

Module Learning Outcomes

On completing the Introduction to Machine Learning module, students will be expected to be able to:

1. Understand what a machine learning informed alert model is and how one can use it to better inform. certain types of decisions to mitigate operational and other banking risks.

2. Be aware of various alert model algorithms to elicit information from data with a view to better informing decisions in banking and financial services settings.

3. Have learned how to evaluate the performance and reliability of alert models, whether proprietorial alert models or otherwise.

4. Be aware of ethical aspects of data mining and alert model deployment as well as the economic, institutional and regulatory contexts of these models in respect to various applications.

Workshops and topics

The module covers multiple topics across six workshops.

Workshop 1 (Remote): Introduction to Machine Learning (with Prof Muckley)

· Overview of the Module

· A schema to introduce machine learning informed alert models

· Mini Lecture 1: AI Use cases across industry sectors and in banking and finance

· Introduction to RapidMiner Tutorial (1)

Workshop 2 (with Prof Muckley)

· Revision of key aspects in workshop 1

· Introduction to RapidMiner Tutorial (2)

· Mini Lecture 2: Special Focus in Elder Financial Exploitation and AI

• Mini Lecture 3: What is the machine learning innovation?

• Mini Lecture 4: What is the role of management?

• Mini Lecture 5: Why do machine learning models fail?

• Preparation for Team Assignment

Workshop 3 (with Prof Muckley)

• Revision of key aspects in workshop 1-2

• Introduction to RapidMiner Tutorial (3)

• Mini Lecture 6: Two Illustrative algorithms: KNN and Decision Trees

• Mini Lecture 7: Data, Software, Terminology and Career Opportunities

• Mini Lecture 8: Performance Evaluation 1 of 2

• Preparation for Team Assignment

Workshop 4 (with Prof Muckley)

• Revision of key aspects in workshops 1-3

• Introduction to RapidMiner Tutorial (4)

• Mini Lecture 9: Performance evaluation 2 of 2

• Ethics of machine learning

• Data visualisation

• Preparation for Team Assignment

Workshop 5 (with Prof Muckley)

· Revision of key aspects in workshops 1-4

· Introduction to RapidMiner Tutorial (5)

· Assessment: Practice Team presentation

Workshop 6 (with Mr Yusra Anas)

· Revision of workshop 1

Workshop 7 (with Mr Yusra Anas)

· Revision of workshop 2

Workshop 8 (with Mr Yusra Anas)

· Revision of workshop 3

Workshop 9 (with Mr Yusra Anas)

· Revision and formal presentations of group assignment.

Workshop 10 (with Mr Yusra Anas)

· Revision of workshops

Workshop 11 (Remote): Conclusion (with Prof Muckley)

· Revision of key aspects in workshops 1-5

PART 3:  MODULE DELIVERY SCHEDULE

The module delivery relies on students’ ability to engage in prior preparation, to seek confirmation and clarification as appropriate and to be actively engaged during the sessions.

*This class is conducted on online.

Class schedule

Preparation Required in Advance of Sessions / Seminars

Students are expected to read each of the assigned references in advance of each lecture.

Student Engagement

During the sessions, students are expected to be able to discuss issues arising from the assigned topics and readings as scheduled above.  Session participation is a vital element in the design of this module and students learning is enhanced by doing so.  Therefore, all students are expected to engage in class discussion and debate in order to facilitate the formation of their critical judgements.

To support your learning, a deck of slides will be available which (on certain occasions) may need to be upgraded / modified during or following the sessions depending on the issues raised.

Students are advised to consider using virtual platforms for student-to-student work or team assignment preparation.

Office Hours Arrangements

I will be available both before and after class on each day should you wish to meet me individually to discuss any aspect of this module. However, I am also available during the class (i.e., during breaks) or you can email me. This contact also does not end upon completion of the last class.

PART 4:  ASSESSMENT DETAILS

This module has two assessment components with specific weightings and marks awarded totalling 100.  These can be seen below:

Assignments

Table 3: Assessment Details