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.
- Lecturer: Rui Zhao, diszr@nus.edu.sg
- TA: Xiaohui Liu, xiaohuiliu@u.nus.edu
- TA: Dingyu Shi, dingyushi@u.nus.edu
- Basic knowledge in Python programing
- Basic math knowledge
The following books are helpful, but not required. You will easily get these books from Internet.
- Deep Learning Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Machine Learning: A Probabilistic Perspective Kevin P. Murphy
- Foundations of Statistical Natural Language Processing Christopher D. Manning and Hinrich Schütze
- Neural Network Methods for Natural Language Processing Yoav Goldberg
- Introduction to Computation and Programming Using Python : With Application to Understanding Data John V. Guttag
If you are not proficient in python, you may find some tutorials helpful.
- Timetable
- Policy
- Code Repo
- Final project
- Canvas: please check that you are enrolled.
- Honor Code
During some lectures, you will be asked to check in. It might be in-class quiz or other forms of assignments.
There are three weekly 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%)
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](project/BT5153_ProjectGuidelines_Grading Criteria.pdf).
- Credit:
- Project proposal (5%)
- Project presentation (20%)
- Project final report (15%)
Class Venue: COM1-0204
| Date | Topic | Content | Assignment |
|---|---|---|---|
| Fri 01/19 | Introduction to Machine Learning and its Production | Link | N.A. |
| Fri 01/26 | Data Preparation | Link | Assignment I Out |
| Fri 02/02 | Machine Learning Modelling | Link | Form your team |
| Fri 02/09 | NO CLASS (CNY) | TBU | N.A. |
| Fri 02/16 | Machine Learning Evaluation | Link | Assignment II Out |
| Fri 02/23 | Machine Learning Deployment | Link | N.A. |
| Sun 03/03 | Recess Week | N.A. | Proposal Due |
| Fri 03/08 | Explainable Machine Learning | Link | Assignment III Out |
| Fri 03/15 | From BoW to Word2Vec | Link | Kaggle Starts |
| Fri 03/22 | From Word2Vec to Transformers | Link | N.A. |
| Fri 03/29 | NO CLASS (Good Friday) | TBU | N.A. |
| Fri 04/05 | LLM and its Practices I | Link | Kaggle Competition |
| Fri 04/12 | LLM and its Practices II | Link | Kaggle Report |
| Fri 04/19 | Why do ML Projects Fail in Business | Link | N.A. |
| Sun 04/28 | Reading Week | N.A. | Presentation and Final Report Due |
