• Portfolio of in-semester worksheets 
• Grading exam

• Blackboard
• Unconfirmed practical marks when available
• Exam Papers, past 2 years (where applicable)

This unit aims to:

• Develop students' algorithmic thinking and their ability to analyse the efficiency of algorithms;
• Enable students to find different approaches for dealing with challenging computational problems that one faces in real-life;
• Provide insight into cutting-edge research-led teaching in modern subfields of algorithms theory, including modern computational models that deals with massive datasets.

By the end of the module the student will be able to:  

• Demonstrate what constitutes an efficient and an inefficient solution to a computational problem;
• Analyse the efficiency of advanced algorithms;
• Evaluate and choose appropriate ways of dealing with challenging computational problems;
• Identify and apply design principles for the design of advanced efficient algorithms;
• Describe efficient algorithms for a range of computational problems, along with their computational complexity.

The main topics of this course are:

1.  Linear program and applications 
2.  Randomization: Hashing and sampling 
3.  Approximation algorithms
4.  Algorithms for large data: Streaming and online algorithms 
5.  Advanced data structures including dynamic data structures

Restricted to COM students.

Optional modules within the department have limited capacity. We will always try to accommodate all students but cannot guarantee a place. 

• The theoretical background and techniques will be presented in a series of formal lectures. Students will reinforce the material through self-study, and through biweekly problems classes where students will apply analytical skills obtained in class. Both lectures and problem classes are designed to achieve the intended learning objectives 1-5.