Note: This repo is for Computer engineering with PR, CG and CV as electives. A more general Format will come later
| Subject | Subject-Code | Subject Type | Credits |
|---|---|---|---|
| Software Engineering | (CO-301) | Departmental Core Course | 4 |
| Theory of Computation | (CO-303) | Departmental Core Course | 4 |
| Computer Graphics | (CO-313) | Department/General Elective Course | 4 |
| Pattern Recognition | (IT-307) | Department/General Elective Course | 4 |
| Computer vision | (EC-353) | University Elective Course | 3 |
| Professional Ethics and Values | (HU-303) | Humanities/Social Science/Management Course | 2 |
| 21 |
2K15 COE-A with CG-B1, PR-A1, CV-A can find their time table here
Update: 20-sept-2017 Mid Semester Datesheet
->rest to be added later<-
- Software engineering, K.K Aggarwal, Yogesh Singh this the main and only text for the course.
- The code files for the lab work can be found in this directory.
- As the lab work is project oriented, where your build on top of the work done in the previous lab, this should only be used as a guide to developing the work.
- Most exam oriented text gets covered in class and the class notes are sufficient for passing.
No single book can be recommended for this course as the scope for questions in the subject is vast.
- Introduction to Computer Theory, Daniel I A Cohen this is the main text for theory.
- Theory of Computer Science: Automata, Languages and Computation, KLP Mishra Follow thos book mainly for the examples and as a quick refresher for Cohen.
- There is no lab for this subject.
- Class notes are sufficient only for getting a grasp of the concepts.
- For theory Cohen[1] should be referred.
- For practice, both Cohen[1] and KLP[2] should be referred.
- Computer Graphics, C Version 2E for theory and algorithms. This is the main text for the course.
- Schaum Outline Computer Graphics this is for numerical practice ONLY. DO NOT FOLLOW FOR THEORY.
- The code files for the lab work can be found in this directory.
- The code work is in OpenGL. Instructions for installing OpenGL on Windows and Linux can be found easily.
- If graphics.h is to be used, follow this tutorial for installing graphics.h on Linux.
- Pattern Classification, 2ed, Duda, Hart, Stork For the basics.
- Pattern Recognition and Machine Learning, Bishop Reference book (recommended for in depth details).
- Neural Networks for Pattern Recognition Support material.
- Apart from Duda, the other two books are fairly expensive(both nearly 4000 Rs.). It's recommended to get them from the library early as there's only a few copies of them available.
- The lab work is done in Python and it is recommended to know/learn the basics early on into the course Here is a good vedio series to do so. The Jupyter Notebooks for the labs can be found here
- It's also recommened to brush up your linear algebra and basic calculus as well.
- Computer Vision: A Modern Approach, Ponce & Forsyth This is the main and only text for the course.
- There is no lab work for this subject (Because of it being an OEC).
- It's better to learn OpenCV on the side even if there's no Lab involved.
- ->to be added<-
- Professional Ethics, R. Subramanian.
- A Textbook on Professional Ethics and Human Values, R.S. Naagarazan
->to be added<-