IT 342: Pattern Recognition

Advice: Please, LOVE and UNDERSTAND probability, statistics, and linear algebra before you register for this course.

Resources

• Course syllabus.
• Video Lectures.
• Link to book webpage. (You can download it in pdf format!). Other two great references (not mandatory for the course) are Duda, Hart, and Stork; and Bishop.
• Lecture Notes
• Code and Programs.
• List of useful texts.
• UCI Machine Learning Repository has many real-life data sets.
• DataCamp assignments

Lectures:

01.	Introduction
02.	Introduction to Statistical Decision Theory I (Regression)
03.	Introduction to Statistical Decision Theory II (Classification)
04.	Introduction to Statistical Decision Theory III (Classification)
05.	Getting to "Learning" I (Regression)
06.	Getting to "Learning" II (Classification)
07.	Linear Models for Regression: Least Mean Square
08.	Linear Models for Regression: Centered Model
09.	Linear Models for Regression: Performance
10.	Linear Models for Regression: Data Preprocessing and Transformation
11.	Bias-Variance Decomposition
A1.	fast revision on basics of Probability
12.	fast revision on basics of Statistics

Assignments

1. Ch2, Duda, Hart, and Stork: solve 2, 6, 7, 8, 9, 27, 33; and computer exercises 2, 3.
2. LLR simulation
3. Linear models
4. Linear models (cont.)
5. Linear models (cont.), and this one
6. Linear models for classification
7. Linear models for classification (2)

**Problems on Appendix (revision):**

9. Review the basics of Probability and Statistics then solve HW1, HW2, and HW3

Suggested Projects

Please, download guidelines, and suggested projects.

Announcements

Please, find here samples for exams.

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