Free Online Course by MIT OpenCourseWare on Computer Algorithms in Systems Engineering

MIT OpenCourseWare is offering a free online course on the theme of “Computer Algorithms in Systems Engineering”.

This course covers concepts of computation used in analysis of engineering systems. It includes the following topics: data structures, relational database representations of engineering data, algorithms for the solution and optimization of engineering system designs (greedy, dynamic programming, branch and bound, graph algorithms, nonlinear optimization), and introduction to complexity analysis. Object-oriented, efficient implementations of algorithms are emphasized.

Prerequisites

1.001 Introduction to Computers and Engineering Problem Solving is the prerequisite, which has 18.01 Calculus as a prerequisite. 6.005 Elements of Software Construction is also accepted. If you haven’t taken 1.001 and don’t know Java or C++ or C#, you may take 1.204 if you attend at least the first month of 1.001 as a listener. You should have some prior programming experience if you do this.

Course Requirements

7 problem sets, each worth 10 points (70% of grade).

2 quizzes (open book, open notes), each worth 15 points (30%). The second quiz may be replaced by a problem set if the class does well on quiz 1 and opts for the extra homework.

Topics:

• Databases

Database: Entity-relationship modeling

Database: Normalization, SQL basics

Database: SQL joins, views, subqueries

Database: JDBC

• Data Structures

Algorithms: Analysis, complexity

Graphs

Heaps, sets

• Divide-and-Conquer and Greedy Algorithms

Divide-conquer: Binary search, quicksort, selection

Greedy: Knapsack, job sequence

Greedy: Minimum spanning trees

Greedy/dynamic programming: Shortest paths

Quiz 1

• Dynamic Programming

Priniciple of optimality, resource allocation

Job scheduling, graph/tree generation

Knapsack problem, set representation

• Branch-and- Bound

Branch and bound: Backtrack method, knapsack problem

Branch and bound: General method, facility location

• Linear and Nonlinear Optimization

Continuous constrained nonlinear optimization

Network equilibrium

Linear systems

Continuous unconstrained nonlinear optimization

• Approximate Queues and Network Designs

Analytic approximations: Network design

Approximate queuing analysis

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