BSc in Computer Engineering
AmirKabir University of Technology (Tehran Polytechnic)

  • CGPA: Overall 18.47 / 20
  • Selected courses 19.33 / 20

All Grades

Download Transcript.pdf, Categorized Grades List.xlsx

Selected Courses

Theoretical Courses

Course NameScoreGPAUnit
Math. (I)19.543
Math. (II)1943
Differential Equations19.843
Engineering Mathematics2043
Engineering Statistics2043
Stochastic Processes (I)2043
Discrete Structures17.543
Data Struct. & Algorithms2043
Design of Algorithms2043
Topics in Computer Science (Algorithm II)2043
Theory of Machines & Languages2043
Design of Programming Language1943
Principles of Compiler Design18.643
GPA in 2019.49439
Course NameScoreGPAUnit
Artificial Intelligence2043
Foundations of Data Mining1943
Data Storage & Retrieval2043
Principles of Database Design18.443
Principles of Computer & Prog.1744
Advanced Computer Programming19.543
BSc Project2043
GPA in 2019.03422

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.

Probabilistic Graphical Models


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.

Statistical Machine Learning


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.

Introduction to Data Mining


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.

Topics in Computer Science (Algorithm II)


In this course we have been introduced to Computational Geometry, Linear Programming, Online Algorithms, Approximation Algorithms, Some NP complete examples.

An introduction to Stochastic Process


In this course we have learned about random walk, some properties of a continues time and discrete time markov chains and their stationary states.