Machine Learning

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.

is712intro2020_v1

Outline

Supervised Learning (first half)

- Generative Models for Classification
  - Naive Bayes
  - Gaussian discriminant analysis
- Linear Regression
- Linear Models for Classification
  - Perceptron
  - Logistic regression
  - Support vector machines
- Neural Networks
  - Convolutional neural networks
  - Recurrent neural networks

Unsupervised Learning (second half)

- Clustering
  - Gaussian mixture model
  - Hierarchical clustering
- Latent variable models
  - Directed graphical models
  - Latent semantic analysis
  - Latent Dirichlet allocation
- Dimensionality Reduction
  - Component analysis
  - Matrix factorization
  - Auto-encoders
- Variational Inference
  - Variational Auto-Encoders

References

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

- Pattern Recognition and Machine Learning by Christopher Bishop
- Machine Learning: a Probabilistic Perspective by Kevin P. Murphy
- Neural Networks and Deep Learning by Charu C. Aggarwal

Teaching Team

AY2020/2021 Term 1
(Hady Lauw, Zhang Ce, and Niu Yudong)

AY2019/2020 Term 1
(Hady Lauw and Tuan Truong)