It covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) .

Such algorithms are often quite efficient and provide useful bounds on the optimal solutions. The learning will be supported by instructor provided notes, readings from textbooks and assignments.

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MG4C6.1 Foundations of Mathematical Programming: An introduction to linear programming and to the theory of duality. MG4C6.2 Mathematical Programming: Introduction to theory and the solution of linear ...

Introduction to theory of algorithms guided by basic Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

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This module introduces basic concepts of computer programming, through an introduction to problem solving and the development of simple algorithms using the programming language Python. The module ...

The graph below shows the total number of publications each year in Probabilistic Programming and Inference Algorithms. References [1] On the hardness of analysing probabilistic programs.