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%