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Machine Learning


Course Overview

I teach IS712 Machine Learning, which is offered to postgraduate research students (PhD and Masters) at School of Information Systems (SIS), Singapore Management University (SMU). The intended audience for this course are graduate students, with an objective of providing a foundation to access academic papers on machine learning algorithms and their applications. Therefore, in addition to regular lectures and tutorials, students are exposed to the academic literature via critical reading and class presentations. Moreover, to develop an appreciation for the practical concerns in applying machine learning to some real-world tasks, students also work on a course project.

For an overview of the course, check out the following introduction slides that we used in the first week.

Roadmap for IS712 Machine Learning ‎‎‎‎‎‎‎‎‎(circa 2019)‎‎‎‎‎‎‎‎‎

Topics

Supervised Learning
  • Generative Models for Classification
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • Neural Networks
Unsupervised Learning
  • Clustering
  • Dimensionality Reduction
  • Matrix Completion

References

The materials are substantially, though not exclusively, based on the following textbooks:

Teaching Team

AY2019/2020 Term 1

(Hady Lauw and Tuan Truong pictured at AAAI-19)