Prerequisite: C243/A594, C250 recommended, M260 Probability and Statistics.

Textbook: Practical Analysis of Algorithms by D. Vrajitoru, W. Knight, Springer, 2014.

Additional documentation to use or not at your discretion - this is not an exhaustive list:

• T. Cormen et al. (2009): Introduction to Algorithms, MIT Press.
• J. Kleinberg, E. Tardos (2006): Algorithm Design, Addison Wesley.
• A. Levitin (2011): Introduction to the Design and Analysis of Algorithms, Addison Wesley.
• R. Johnsonbaugh, M. Schaefer (2003): Algorithms, Prentice Hall.
• S. Dasgupta et al. (2006): Algorithms, McGraw-Hill.
• R. Neapolitan, K. Naimipour (2011): Foundations of Algorithms, Jones and Bartlett.

Grading system:

• About 11 homework assignments: 20 points each
• 2 midterm exams: 50 points each
• Final exam: 50 points
• Class participation: up to 30 points

Guidelines for assignments:

• The assignments will be posted on the course web page and on Canvas.
• The assignments are due at midnight of the due date.
• The assignments must be submitted to Canvas. Assignments written on paper can be scanned and submitted as images or pdf files.
• No homework accepted after 1 week from the due date. A homework turned in late loses 10% of the points per day up to 50%.
• For problems that have a short answer, such as a number, in general a detailed explanation of the steps taken to obtain that answer is expected.
• Class participation items are not announced beforehand; there is no make up for them.
• Attendance is expected except for health issues. Please quarantine at home if you have Covid symptoms.
• All of the assignments are individual. Consulting with colleagues is acceptable, but homeworkss that are too similar can be penalized or rejected. No credit will be given for homeworks obtained from external sources unless explicitly allowed.