AI Applications in Accounting and Finance

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AI Applications in Accounting and Finance

Dozent: Sean Cao

4.174 bereits angemeldet

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4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

10 Bewertungen

Stufe Anfänger

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

Kategorie: Financial Analysis
Kategorie: Image Analysis
Kategorie: Text Mining
Kategorie: Accounting
Kategorie: Natural Language Processing
Kategorie: AI Integrations
Kategorie: Applied Machine Learning
Kategorie: Social Media Analytics
Kategorie: Financial Statement Analysis
Kategorie: Unstructured Data
Kategorie: Financial Data
Kategorie: Model Evaluation
Kategorie: Machine Learning
Kategorie: Artificial Intelligence
Kategorie: Emerging Technologies
Kategorie: Data-Driven Decision-Making
Kategorie: Finance

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3 Aufgaben

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

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In diesem Kurs gibt es 4 Module

In this course, you will learn:

How to work with unstructured financial data like earnings call transcripts, press releases, and ESG disclosures. How to apply machine learning and AI tools to real-world financial documents, images, and social media signals. How to evaluate and use emerging technologies with minimal coding requirements—no tech background needed. Detailed description: Designed for accounting and finance students and professionals, this course removes the common barriers to learning AI by integrating practical, job-relevant applications directly into the business context you already understand. Using real business data and use cases, you will learn how AI is used in accounting to solve accounting problems and complete accounting tasks. Each module includes engaging video lessons, guided exercises, and accessible tutorials that help you grasp key concepts. By the end, you will not only understand how AI works in finance, you will be able to use it confidently. Whether you are preparing for a career in corporate finance, accounting, consulting, or fintech, this course will give you a competitive edge.

Das ist alles enthalten

20 Videos14 Lektüren1 Aufgabe2 Diskussionsthemen

20 VideosInsgesamt 220 Minuten

The Rising Use of Big Data for Decision-Making7 Minuten
How to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 110 Minuten
How to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 212 Minuten
How to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 37 Minuten
Overview of Academic Research and Industry Adoption, Part 116 Minuten
Overview of Academic Research and Industry Adoption, Part 210 Minuten
An Overview of Structured and Unstructured Data, Part 111 Minuten
An Overview of Unstructured and Structured Data, Part 28 Minuten
An Overview of Unstructured and Structured Data, Part 39 Minuten
Theory-Driven and Machine-Learning Approach of Data Analytics7 Minuten
The Advantages of Applying Machine-Learning Approaches10 Minuten
How Industry and Academics Adopt AI, Part 110 Minuten
How Industry and Academics Adopt AI, Part 212 Minuten
How Industry and Academics Adopt AI, Part 316 Minuten
How Industry and Academics Adopt AI, Part 45 Minuten
How Industry and Academics Adopt AI, Part 57 Minuten
How to Generate and Eliminate Ideas23 Minuten
How Do Writing Styles and Focal Questions Differ Between Finance and Accounting Scholars?12 Minuten
Disruptive AI in Classical Finance Theory, Part 113 Minuten
Disruptive AI in Classical Finance Theory, Part 214 Minuten

14 LektürenInsgesamt 71 Minuten

Course Overview5 Minuten
Content Overview10 Minuten
Video Roadmap10 Minuten
Meet Your Instructor2 Minuten
Getting Help1 Minute
Introduction to Module 1: Data Analytics in Finance and Accounting10 Minuten
The Rising Use of Big Data for Decision-Making3 Minuten
What Separates Us from Computer Science and Statistics Majors: The Importance of Domain Knowledge5 Minuten
Tailoring Data Science to the Needs of Different Corporate Stakeholders3 Minuten
Unstructured Data Analytics3 Minuten
Structured Data Analytics3 Minuten
Theory-Driven Approach Vs. Machine-Learning Approach4 Minuten
The Advantages of Applying Machine-Learning Approaches2 Minuten
Coming Up in "Idea Generation"10 Minuten

1 AufgabeInsgesamt 30 Minuten

Module 1 Quiz30 Minuten

2 DiskussionsthemenInsgesamt 45 Minuten

Meet Your Learning Group15 Minuten
Application and Discussion: Approaches to Data Analytics30 Minuten

Das ist alles enthalten

9 Videos15 Lektüren1 Aufgabe1 Diskussionsthema

9 VideosInsgesamt 77 Minuten

Data Structure of 10-K Filing4 Minuten
Item 1 Business Description6 Minuten
Item 1A Risk Disclosure4 Minuten
Item 7: Management's Discussion and Analysis7 Minuten
Textual Analysis: Keyword Search and LDA, Part 120 Minuten
Textual Analysis: Keyword Search and LDA, Part 27 Minuten
An Empirical Example of Analyzing 10-K Filings11 Minuten
Guidance for Module 2 Project: How to Crawl Annual Reports10 Minuten
Guidance for Module 2 Project: How to Parse Unstructured Data8 Minuten

15 LektürenInsgesamt 97 Minuten

Introduction to Module 2: Analyzing Annual Reports10 Minuten
Data Structure in Annual Reports and the 10-K Filing2 Minuten
Other Items in Form 10-K: Part I of Form 10-K30 Minuten
Other Items in Form 10-K: Part II of Form 10-K15 Minuten
Other Items in Form 10-K: Part III and IV of Form 10-K4 Minuten
Data Structure in Annual Reports1 Minute
Coming Up in "Conventional Textual Analysis Approach"1 Minute
Conventional Approach Review (Bag of Words)1 Minute
Building a Keyword Dictionary5 Minuten
Latent Dirichlet Allocation (LDA)2 Minuten
Coming Up in "Empirical Examples: Analyzing Corporate Filings for Making Business Decisions"10 Minuten
10-Ks and 10-Qs1 Minute
Item 1 of 10-K2 Minuten
Item 1A of 10-K3 Minuten
Guidance for Module 2 Project: Sample Codes10 Minuten

1 AufgabeInsgesamt 30 Minuten

Module 2 Quiz30 Minuten

1 DiskussionsthemaInsgesamt 60 Minuten

Application and Discussion: How to Crawl Annual Reports and Parse Unstructured Data60 Minuten

Das ist alles enthalten

17 Videos21 Lektüren1 Aufgabe1 Diskussionsthema

17 VideosInsgesamt 227 Minuten

Procedures for Applying Machine Learning Models, Part 15 Minuten
Procedures for Applying Machine Learning Models, Part 214 Minuten
Procedures for Applying Machine Learning Models, Part 36 Minuten
Basic Concept and Foundation of ML15 Minuten
Textual Analysis: Word Representation and Sentence Level Analysis using Google Bert, Part 117 Minuten
Textual Analysis: Word Representation and Sentence Level Analysis using Google Bert, Part 29 Minuten
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 17 Minuten
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 215 Minuten
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 312 Minuten
Prompt Engineering for Large Language Models, Part 18 Minuten
Prompt Engineering for Large Language Models, Part 214 Minuten
Prompt Engineering for Large Language Models, Part 312 Minuten
LLM Look-Ahead Bias and Potential Solutions, Part 110 Minuten
LLM Look-Ahead Bias and Potential Solutions, Part 219 Minuten
How AI Differs from Traditional Technology Breakthroughs11 Minuten
Using AI as a Research Assistant, Part 119 Minuten
Using AI as a Research Assistant, Part 234 Minuten

21 LektürenInsgesamt 82 Minuten

Introduction to Module 3: Emerging AI Technology in Textual Analysis10 Minuten
Data Cleaning, Parsing, and Feature Selection2 Minuten
Machine Learning Model Selection2 Minuten
Hyperparameter Tuning3 Minuten
Model Evaluation5 Minuten
Fundamental Concepts of Pre-Training in Machine Learning5 Minuten
Textual Representation3 Minuten
Advantages of Phrase-level Word-Embedding1 Minute
Reinforcement Learning2 Minuten
Pre-Trained Sentence-Level Word-Embedding2 Minuten
Bidirectional Encoder Representation from Transformers2 Minuten
Generative Pre-trained Transformers1 Minute
Differences between GPT and BERT2 Minuten
Application of GPT5 Minuten
Prompt Engineering2 Minuten
Hard Prompt-Tuning5 Minuten
Soft Prompt-Tuning3 Minuten
Prompt Engineering Vs. Model Fine-Tuning3 Minuten
Machine Philosophy4 Minuten
Competitive Advantages of Man and Machine5 Minuten
Using AI as a Research Assistant15 Minuten

1 AufgabeInsgesamt 30 Minuten

Module 3 Quiz30 Minuten

1 DiskussionsthemaInsgesamt 60 Minuten

Application and Discussion: Evaluating Machine Learning Models60 Minuten

Das ist alles enthalten

1 Plug-in

Dozent

Sean Cao

University of Maryland, College Park

1 Kurs4.174 Lernende

von

University of Maryland, College Park

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