Course 2024-2025 a.y.

20985 - NLP AND MACHINE LEARNING FOR BUSINESS DECISIONS

Department of Accounting

Course taught in English

Class timetable
Exam timetable
Go to class group/s: 31
M (6 credits - II sem. - OP  |  SECS-P/07) - IM (6 credits - II sem. - OP  |  SECS-P/07) - MM (6 credits - II sem. - OP  |  SECS-P/07) - AFC (6 credits - II sem. - OP  |  SECS-P/07) - CLELI (6 credits - II sem. - OP  |  SECS-P/07) - ACME (6 credits - II sem. - OP  |  SECS-P/07) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/07) - EMIT (6 credits - II sem. - OP  |  SECS-P/07) - GIO (6 credits - II sem. - OP  |  SECS-P/07) - DSBA (6 credits - II sem. - OP  |  SECS-P/07) - PPA (6 credits - II sem. - OP  |  SECS-P/07) - FIN (6 credits - II sem. - OP  |  SECS-P/07) - AI (6 credits - II sem. - OP  |  SECS-P/07)
Course Director:
EKATERINA NERETINA

Classes: 31 (II sem.)
Instructors:
Class 31: EKATERINA NERETINA


Mission & Content Summary

MISSION

The course is designed as a “hands-on” introduction to Natural Language Processing (NLP) and Machine Learning (ML) applications in business, accounting, and finance. Artificial intelligence-based methods have been gaining popularity in the corporate world, having broad day-to-day applications. For example, such methods are used to identify key performance indicators, relationships and correlations between various documents, unusual patterns in financial reporting, or detect fraud. The mission of this course is to familiarize students with such NLP and ML methods.

CONTENT SUMMARY

Below there is a list of the topics covered in the course:

  • Introduction to Natural Language Processing (NLP) and Machine Learning (ML) methods, their common forms and present-day business applications.
  • Obtaining and preprocessing textual data.
  • Representing textual data mathematically.
  • Text classification.
  • Extraction of valuable information from text.
  • NLP applications in accounting, finance, law, and other practice areas and industries.
  • Ethics in NLP.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

tbd

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

After successful completion of this course, students will be able to:  

  • Apply core NLP and ML methods in various business environments and practice areas such as accounting, finance, etc.
  • Discuss key theoretical concepts behind the methods.
  • Develop web scrapers to extract textual data from websites with publicly available business information.
  • Analyze textual data with the purpose of making business decisions.
  • Justify the use of chosen NLP methods for specific business purposes.
  • Apply machine learning models, evaluate the model performance, and interpret the output with the purpose of making business decisions.

Teaching methods

  • Lectures
  • Practical Exercises
  • Individual works / Assignments

DETAILS

Exercises: Writing Python code snippets, testing them using real-world data, and interpreting their outcomes.

Case studies: Most topics will be accompanied by brief case studies with code snippets that students can utilize in the future.

Individual assignments: An individual assignment (“take-home test”) will be a part of the grading scheme (please see below).


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
   x x

ATTENDING AND NOT ATTENDING STUDENTS

ATTENDING

 

For the first time student take the exam, the grade will be determined as the best between (A) a combination of your grade on one “take-home” test (with a weight of 25% of the total grade) plus the final exam (weight of 75%), and (B) 100% weight on the final exam.

For any retake, the grade will be entirely determined based on the final exam (i.e., its weight will be 100%).

The “take-home” test will be distributed and accessible via Blackboard.

The final exam is written, and based on the material discussed in class.

 

NOT ATTENDING

 

The grade will be entirely based on the final exam.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

For a large part of the course, the course will use

Vajjala, S., Majumder, B., Gupta, A., & Surana, H. (2020). Practical natural language processing: a comprehensive guide to building real-world NLP systems. O'Reilly Media.

Other required textbooks and materials will be posted on Blackboard before the start of the course. Any required textbooks will be in the eBook format.

Last change 11/09/2024 10:45