BSc in Computer Engineering
AmirKabir University of Technology (Tehran Polytechnic)
- CGPA: Overall 18.47 / 20
- Selected courses 19.33 / 20
|Stochastic Processes (I)||20||4||3|
|Data Struct. & Algorithms||20||4||3|
|Design of Algorithms||20||4||3|
|Topics in Computer Science (Algorithm II)||20||4||3|
|Theory of Machines & Languages||20||4||3|
|Design of Programming Language||19||4||3|
|Principles of Compiler Design||18.6||4||3|
|GPA in 20||19.49||4||39|
Data and Search related Courses
|Foundations of Data Mining||19||4||3|
|Data Storage & Retrieval||20||4||3|
|Principles of Database Design||18.4||4||3|
|Principles of Computer & Prog.||17||4||4|
|Advanced Computer Programming||19.5||4||3|
|GPA in 20||19.03||4||22|
More information about some courses
In the Artificial Intelligence, Stochastic Process, Algorithm Desing, Topics in Computer Science (Algorithm II), and Theory of Machines and Languages courses, I have got the distinctive highest grade in the class.
This graduate level course is based on Koller’s PGMs book. In representation part, inference part, and learning part of the book, the have learned mostly chapter 3-5, 9-13, and 17-18 respectively. Through this course, we have learned graphical representation and its properties, ways to estimate posterior distribution in reasonable time (e.g. MCMC), and how to predict parameters and graphical structures. I have audited this course.
This graduate level course is based on Wasserman’s All of The Statistics book. The first 14 Chapters of the book were covered. We have learned topics like: Different types of convergence, Parametric and Non Parametric learning, Bootstrap technique for finding confidence interval, parameter inference and model selections. I have audited this course.
In this course, we have learned different techniques to use data to for prediction. We have learned regression, decision trees, enrtopy concepts, perceptron, evalutaion metrics like ROC, and clustering techniques.
In this course we have been introduced to Computational Geometry, Linear Programming, Online Algorithms, Approximation Algorithms, Some NP complete examples.
In this course we have learned about random walk, some properties of a continues time and discrete time markov chains and their stationary states.