This graduate-level algorithms class was the last class for my Master’s in Science degree at Georiga Tech. As part of the ML specialization for the Computer Science degree, I had to take CS6515 Introduction to Graduate Algorithms. During this fall semester, I was working full time. If you’re also expecting to be working full-time during the week or you’d like to have my tips and tricks for doing well in this course, this review should help you.
Course Content and Information
The goal of the course is to give students a graduate-level introduction to the design and analysis of algorithms. The main topics covered in the 16-week course include dynamic programming; divide and conquer, including FFT; randomized algorithms, including the RSA cryptosystem, graph algorithms; max-flow algorithms; linear programming; and NP-completeness.
The course counts towards many of the computer science specializations and it touches many concepts that are covered in other courses found throughout the degree program. Most people take this course as the last class for the degree program. As such, this course was one of the most well documented, supported, and facilitated by the course staff.
Take note that there is no curve for this course and the cutoffs for the semester were the following:
A: [85%, 100%] B: [70%, 85%) C: [50%, 70%) D: [40%, 50%) F: [0%, 40%)
The course consisted of assignments that I’ll discuss in greater detail.
Homework: 7 turned in through Gradescope — 12% total.
Polls: 8 hosted in Canvas — 4% total.
Coding projects: 3 turned into Gradescope— 9% total.
Three exams: proctored through Honorlock — 25% each.
Final exam (optional).
Each assignment was due on Monday, 8 am Eastern time zone, no late submissions were accepted.
The homework assignments included two problems that you’ll submit into Gradescope for grading by the course staff. Grading was straightforward and any deductions on the homework were meant to course…