Everything I Learned in CS at UT Austin: Introduction

In less than two months, I will be graduating with a B.S. in computer science from the University of Texas at Austin. In order to prevent everything I’ve learned from immediately vanishing from my brain, I’m beginning a series of essays that recaps every class I’ve taken. Hopefully, these essays will also give an insight into the actual content of a computer science degree and prove valuable for prospective computer science students at UT Austin or anywhere else.

Important Disclaimers

Before I begin, however, I would like to make a few disclaimers:

• Errors May Occur: There may be errors at any point in the series. I am not graduating with a 4.0 GPA, and even if I was, the sheer size of the content that will be covered in this series of essays will likely cause some things to slip through the cracks.
• Non-Comprehensive: This series is not comprehensive. I will only describe the concepts from each course at a high level for brevity’s sake and to avoid infringing on any of the university’s rules.
• Varying Detail: It is possible that certain courses or certain topics within each course overview will be explained in different amounts of detail. While I do my best to treat all topics equally, I am simply more well-versed in some topics than in others.
• CS Courses Only: I will discuss only the computer science courses I took over the course of my degree.
• Order of Essays: The order of the courses discussed in this series is not the order in which I took them. They are loosely organized so similar courses follow in sequence.

The Essay Series Structure

There will be a total of seven essays, covering the following course groups:

1. Data Structures and Discrete Math
2. Computer Architecture and Algorithms and Complexity
3. Principles of Computer Systems and Computer Networks
4. Data Management and Information Retrieval
5. Ethical Foundations of Computer Science and Software Engineering
6. Principles of Machine Learning I and Artificial Intelligence
7. Natural Language Processing and Neural Networks