К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
AI Applications in Accounting and Finance · LearnSpace
Назад в каталог
courseraБизнес

AI Applications in Accounting and Finance

Курс от University of Maryland, College Park
Начальный≈ 17.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Artificial IntelligenceText MiningMachine LearningFinancial AnalysisFinancial Statement AnalysisUnstructured DataFinancial DataAI IntegrationsAccountingImage AnalysisFinanceEmerging TechnologiesNatural Language ProcessingData-Driven Decision-MakingSocial Media AnalyticsModel EvaluationApplied Machine Learning

Программа курса

4 модулей · 104 учебных материалов

01Course Introduction and Module 1: Data Analytics in Finance and Accounting37 материалов

Course Introduction and Module 1 Introduction

Course OverviewЧтениеContent OverviewЧтениеVideo RoadmapЧтениеMeet Your InstructorЧтение

Учитесь у экспертов

Sean Cao

Преподаватель курса

AI Applications in Accounting and Finance
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 17.2 ч

4 модулей

Язык: Английский

Субтитры: Венгерский, Казахский

Часть программы вашего университета
Getting HelpЧтение
Meet Your Learning GroupОбсуждение
Introduction to Module 1: Data Analytics in Finance and AccountingЧтение

1.1 How to Leverage Data Science for Corporate Stakeholders

The Rising Use of Big Data for Decision-MakingЧтениеThe Rising Use of Big Data for Decision-MakingВидеоHow to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 1ВидеоHow to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 2ВидеоHow to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 3ВидеоWhat Separates Us from Computer Science and Statistics Majors: The Importance of Domain KnowledgeЧтениеTailoring Data Science to the Needs of Different Corporate StakeholdersЧтениеOverview of Academic Research and Industry Adoption, Part 1ВидеоOverview of Academic Research and Industry Adoption, Part 2Видео

1.2 Overview of Structured and Unstructured Data

Unstructured Data AnalyticsЧтениеStructured Data AnalyticsЧтениеAn Overview of Structured and Unstructured Data, Part 1ВидеоAn Overview of Unstructured and Structured Data, Part 2ВидеоAn Overview of Unstructured and Structured Data, Part 3Видео

1.3 Theory-Driven and Mchine-Learning Approach of Data Analytics

Theory-Driven Approach Vs. Machine-Learning ApproachЧтениеTheory-Driven and Machine-Learning Approach of Data AnalyticsВидеоThe Advantages of Applying Machine-Learning ApproachesЧтениеThe Advantages of Applying Machine-Learning ApproachesВидео

1.4 Idea Generation

Coming Up in "Idea Generation"ЧтениеHow Industry and Academics Adopt AI, Part 1ВидеоHow Industry and Academics Adopt AI, Part 2ВидеоHow Industry and Academics Adopt AI, Part 3ВидеоHow Industry and Academics Adopt AI, Part 4ВидеоHow Industry and Academics Adopt AI, Part 5ВидеоHow to Generate and Eliminate IdeasВидеоHow Do Writing Styles and Focal Questions Differ Between Finance and Accounting Scholars?ВидеоDisruptive AI in Classical Finance Theory, Part 1ВидеоDisruptive AI in Classical Finance Theory, Part 2Видео

Module 1 Activities

Module 1 QuizЗаданиеApplication and Discussion: Approaches to Data AnalyticsОбсуждение
02Module 2: Analyzing Annual Reports26 материалов

Module 2 Introduction

Introduction to Module 2: Analyzing Annual ReportsЧтение

2.1 Data Structure in Annual Reports and 10-K Filings

Data Structure in Annual Reports and the 10-K FilingЧтениеData Structure of 10-K FilingВидеоOther Items in Form 10-K: Part I of Form 10-KЧтениеItem 1 Business DescriptionВидеоItem 1A Risk DisclosureВидеоItem 7: Management's Discussion and AnalysisВидеоOther Items in Form 10-K: Part II of Form 10-KЧтениеOther Items in Form 10-K: Part III and IV of Form 10-KЧтениеData Structure in Annual ReportsЧтение

2.2 Conventional Textual Analysis Approach

Coming Up in "Conventional Textual Analysis Approach"ЧтениеTextual Analysis: Keyword Search and LDA, Part 1ВидеоTextual Analysis: Keyword Search and LDA, Part 2ВидеоConventional Approach Review (Bag of Words)ЧтениеBuilding a Keyword DictionaryЧтениеLatent Dirichlet Allocation (LDA)Чтение

2.3 Empirical Examples: Analyzing Corporate Filings for Making Business Decisions

Coming Up in "Empirical Examples: Analyzing Corporate Filings for Making Business Decisions"ЧтениеAn Empirical Example of Analyzing 10-K FilingsВидео10-Ks and 10-QsЧтениеItem 1 of 10-KЧтениеItem 1A of 10-KЧтение

Module 2 Activities

Module 2 QuizЗаданиеApplication and Discussion: How to Crawl Annual Reports and Parse Unstructured DataОбсуждениеGuidance for Module 2 Project: Sample CodesЧтениеGuidance for Module 2 Project: How to Crawl Annual ReportsВидеоGuidance for Module 2 Project: How to Parse Unstructured DataВидео
03Module 3: Emerging AI Technology in Textual Analysis40 материалов

Module 3 Introduction

Introduction to Module 3: Emerging AI Technology in Textual AnalysisЧтение

3.1 Procedures for Applying Machine Learning Models

Data Cleaning, Parsing, and Feature SelectionЧтениеMachine Learning Model SelectionЧтениеHyperparameter TuningЧтениеModel EvaluationЧтениеProcedures for Applying Machine Learning Models, Part 1ВидеоProcedures for Applying Machine Learning Models, Part 2ВидеоProcedures for Applying Machine Learning Models, Part 3Видео

3.2 Fundamental Concepts of Pre-Training in Machine Learning

Fundamental Concepts of Pre-Training in Machine LearningЧтениеBasic Concept and Foundation of MLВидео

3.3 Pre-Trained Phrase-Level Word Embedding

Textual RepresentationЧтениеAdvantages of Phrase-level Word-EmbeddingЧтение

3.4 Reinforcement Learning

Reinforcement LearningЧтение

3.5 Pre-Trained Sentence-Level Word Embedding with BERT, GPT, and DeepSeek

Pre-Trained Sentence-Level Word-EmbeddingЧтениеBidirectional Encoder Representation from TransformersЧтениеTextual Analysis: Word Representation and Sentence Level Analysis using Google Bert, Part 1ВидеоTextual Analysis: Word Representation and Sentence Level Analysis using Google Bert, Part 2ВидеоGenerative Pre-trained TransformersЧтениеDifferences between GPT and BERTЧтение

3.6 Prompt Engineering for Large Language Models

Prompt EngineeringЧтениеHard Prompt-TuningЧтениеSoft Prompt-TuningЧтениеPrompt Engineering Vs. Model Fine-TuningЧтениеPrompt Engineering for Large Language Models, Part 1ВидеоPrompt Engineering for Large Language Models, Part 2Видео

3.7 Man and Machine

Machine PhilosophyЧтениеCompetitive Advantages of Man and MachineЧтениеHow AI Differs from Traditional Technology BreakthroughsВидеоUsing AI as a Research AssistantЧтениеUsing AI as a Research Assistant, Part 1ВидеоUsing AI as a Research Assistant, Part 2Видео

Module 3 Activities

Module 3 QuizЗаданиеApplication and Discussion: Evaluating Machine Learning ModelsОбсуждение
04Course Wrap-Up1 материалов
End of Course SurveyPLUGIN
Application of GPTЧтение
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 1Видео
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 2Видео
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 3Видео
Prompt Engineering for Large Language Models, Part 3Видео
LLM Look-Ahead Bias and Potential Solutions, Part 1Видео
LLM Look-Ahead Bias and Potential Solutions, Part 2Видео