204453 Pattern Recognition

(Section 701)

General information

Class meeting: Tue-Fri 9.30-11.30
Lecture @ CSB210
LAB @ CSB308
Teaching format: Hybrid (online via MS Teams)
Main communication channel: MS Teams
Tools: Python programming language + Scikit-learn

Books

Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.
Murty, M. N., Devi, V. S.: Pattern Recognition: An Algorithmic Approach (Undergraduate Topics in Computer Science). Springer (2012)

Schedule

21 June

No class

24 June

Course admin

28 June

Case studies

1 July

Introduction to Pattern Recognition

5 July

Pre-processing lab

8 July

Feature representation and selection

12 July

Feature extraction lab

15 July

No class

19 July

Nearest neighbour

22 July

Decision tree

26 July

Decision tree lab

29 July

No class

2 August

Introduction to term project

5 August

Image representation / Feature extraction

9 August

Image representation / Feature extraction lab

Colab notebook -- Images

12 August

No class

16 August

Baye classifier lab

19 August

Bayes classifier

23 August

Exam preparation (No class)

26 August

Exam preparation (No class)

Midterm Examination -- 2 September 2022, 12:00 - 15:00, online via Exam Moodle

6 September

No Class

9 September

Ensemble methods

13 September

Ensemble methods lab

16 September

Neural networks

20 September

Neural network lab

23 September

Deep learning

27 September

Deep learning lab (CNN)

chest - dataset

30 September

Project discussion

4 October

Deep learning lab (LSTM)

7 October

No class

11 October

No class

14 October

Holiday

18 October

Project presentation

Final exam: 2 November 2022 @ 8.00 - 11.00

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