BT5153 Applied Machine Learning for Business Analytics

NUS, MSBA / Spring 2026

Content

This course provides a comprehensive overview of advanced machine learning techniques, focusing on practical applications in business analytics. Emphasizing intuitive understanding, it covers trending machine learning models, particularly in Natural Language Processing (NLP). Students will engage in hands-on learning, exploring feature engineering, model selection, training, and the development of end-to-end machine learning projects. For sure, the curriculum includes a special segment on Large Language Models (LLMs). Python will be the primary programming language used for instruction and project implementation. The course aims to equip students with both theoretical knowledge and practical skills for real-world challenges.

Contact Information:

• Lecturer: Rui Zhao, diszr@nus.edu.sg
• TA: Dingyu Shi, dingyushi@u.nus.edu
• TA: Yang Ding, yding@u.nus.edu

Prerequisites:

• Basic knowledge in Python programing
• Basic math knowledge

References

To build a strong understanding of NLP and deep learning, I recommend the following books, blogs & papers.

Recommended Books

1. Hands-On Large Language Models: Language Understanding and Generation

2. Dive into Deep Learning

3. Deep Learning

Blogs & Videos & Podcasts

In addition to books, there are excellent blogs that can deepen your understanding:

• Hugging Face Blog – Covers state-of-the-art NLP models and techniques.
• Cameron’s Blog – Great for recent advancements in NLP.
• Jay Alammar’s Blog – Excellent visual explanations of transformers and deep learning concepts.
• Lilian Weng’s Blog – Covers a wide range of topics in AI and LLMs.
• Andrej Karpathy’s Youtube – High-quality videos on deep learning and LLMs.
• How I AI - How to actually use AI/new tools to improve the quality and efficiency of their work
• Deep Learning Focus - Some in-depth technicial discussions about DL/LLM concepts

Research Papers

• LLM Paper List from Andrej Karpathy

If you are not proficient in python, you may find some tutorials helpful.

Course material and links

• Timetable
• Policy
• Code Repo
• Final project
• Canvas: please check that you are enrolled.
• Honor Code

Assessment

Attendance Check (10%)

During some lectures, you will be asked to check in. It might be in-class quiz or other forms of assignments.

Individual Assignments (50%)

There are three assignments and a mini Kaggle competition. Students are expected to complete these individual tasks to gauge their understanding of the course materials so as to prepare them for their Group Project and future data science tasks. Details of the individual assignments will be updated later.

• Credit:
  • Assignment 1 (10%)
  • Assignment 2 (10%)
  • Assignment 3 (10%)
  • Kaggle Competition (20%)

Group Project (40%)

You are required to form a project group with 4-5 members. Students can form their own teams and please fill out the google sheets. If a student can’t find a partner, we will team you up randomly (send the email to our TAs). Your project task is to apply the data mining and machine learning techniques that you have acquired to gain insights and draw interesting conclusions to a (business) problem. You are to apply (advanced) data mining and analytics tools (preferably in Python as Python tools are used as supplementary aids during the delivery of this course) to process structured and unstructured data available on the Web. You will then summarize your insights and present your conclusions using suitable visual aids. More detailed information can be found here.

• Credit:
  • Project proposal (5%)
  • Project presentation (20%)
  • Project final report (15%)

Schedule

Class Venue: COM1-0204

Date	Topic	Content	Assignment
Fri 01/16	Introduction	Link	N.A.
Fri 01/23	Text Representations: BoW to Word2Vec	Link	N.A.
Fri 01/30	Transformers	Link	Cancelled. Make-up: 02/06 & 02/13
Fri 02/06	LLM Fundamentals	Link	Assignment I Out
Fri 02/13	Training & Scaling LLMs	Link	Assignment II Due
Fri 02/20	Inference & Reasoning	Link	Assignment II Out
Fri 02/27	Recess Week	N.A.	Proposal & Assignment II Due
Fri 03/06	RAG & Context Management	Link	Assignment III Out
Fri 03/13	Agent Design Patterns	Link	Assignment III Due
Fri 03/20	Agent Production&Security	Link	Kaggle Starts
Fri 03/27	ML Model Evaluation	N.A.	N.A.
Fri 04/03	Good Friday	N.A.	N.A.
Fri 04/10	ML Model Deployment	Link	Kaggle Competition
Fri 04/17	Why ML&LLM Projects Fail	Link	N.A.
Fri 04/24	N.A.	N.A.	Kaggle Report
Fri 05/01	N.A.	N.A.	Presentation and Final Report Due