# Education

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 Name | Score | GPA | Unit |
---|---|---|---|

Math. (I) | 19.5 | 4 | 3 |

Math. (II) | 19 | 4 | 3 |

Differential Equations | 19.8 | 4 | 3 |

Engineering Mathematics | 20 | 4 | 3 |

Engineering Statistics | 20 | 4 | 3 |

Stochastic Processes (I) | 20 | 4 | 3 |

Discrete Structures | 17.5 | 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

Course Name | Score | GPA | Unit |
---|---|---|---|

Artificial Intelligence | 20 | 4 | 3 |

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 |

BSc Project | 20 | 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.

## Probabilistic Graphical Models

** Published:**

This graduate level course is based on Kollerâ€™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

** Published:**

This graduate level course is based on Wassermanâ€™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

** Published:**

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)

** Published:**

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

** Published:**

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