CZ4041: Machine Learning

Year 4 elective offered in semester 2

Instructors: Prof Sinno Pan and Prof Jagath Rajapakse

SYLLABUS

1. Introduction to machine learning:

Overview of machine learning and its applications

2. Decision
theory and Bayes models:

Bayesian
decision theory, na•ve Bayes, Bayesian brief networks

3.
Classifier evaluation:

Cross-validation,
generalization errors

4.
Classification:

Decision
trees, artificial neural networks, linear and kernelized
support vector machines, K-nearest neighbor classifiers, linear regression and
its kernelized extension

5. Graphical
models:

Bayesian
networks, belief propagation, Markov random fields (MRF), hidden Markov models
(HMM)

6. Clustering:

K-means
clustering, hierarchical clustering, performance evaluation for clustering,
applications

7. Dimension
reduction:

Principal component analysis (PCA), linear discriminant analysis (LDA),
canonical correlation analysis (CCA), ISOMAP

8. Density
estimation:

General
theory for non-parametric density estimation, Parzen
windows, Smooth kernels, nearest neighbors

9. Ensemble
learning:

Boosting,
bootstrapping, model average

10.
Applications (course projects):

Various
application areas of machine learning

**TEXT**

Ethem Alpaydin, *Introduction to Machine Learning (Third
Edition)*, MIT Press, 2014

**ASSESSMENT
**

Projects 40%

Final exam 60%