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

Slides

28 June

Case studies

1 July

Introduction to Pattern Recognition

Slides

5 July

Pre-processing lab

Colab Notebook

8 July

Feature representation and selection

Slides

12 July

Feature extraction lab

Colab Notebook

15 July

No class

19 July

Nearest neighbour

Slides

22 July

Decision tree

Slides

26 July

Decision tree lab

Slides

29 July

No class

2 August

Introduction to term project

Slides

5 August

Image representation / Feature extraction

slides

9 August

Image representation / Feature extraction lab

Colab notebook -- Images

12 August

No class

16 August

Baye classifier lab

Slides Dataset

Google form

19 August

Bayes classifier

Slides

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

Slides

13 September

Ensemble methods lab

Slides

Lab sheet

16 September

Neural networks

Slides

20 September

Neural network lab

Google form

23 September

Deep learning

Slides

27 September

30 September

Project discussion

4 October

Deep learning lab (LSTM)

Google Colab

Data

7 October

No class

11 October

No class

14 October

Holiday

18 October

Project presentation

Final exam: 2 November 2022 @ 8.00 - 11.00