This course focuses on the fundamentals of computer algorithms, emphasizing methods useful in practice. Upon successful completion of this course, the student will be able to: explain and identify the importance of algorithms in modern computing systems and their place as a technology in the computing industry; indentify algorithms as a pseudo-code to solve some common problems; describe asymptotic notations for bounding algorithm running times from above and below; explain methods for solving recurrences useful in describing running times of recursive algorithms; explain the use of Master Theorem in describing running times of recursive algorithms; describe the divide-and-conquer recursive technique for solving a class of problems; describe sorting algorithms and their runtime complexity analysis; describe the dynamic programming technique for solving a class of problems; describe greedy algorithms and their applications; describe concepts in graph theory, graph-based algorithms, and their analysis; describe tree-based algorithms and their analysis; explain the classification of difficult computer science problems as belonging to P, NP, and NP-hard classes. (Computer Science 303)

An introduction to the common types of branching control structures and which ones are allowed in good structured programming.

A tutorial explaining fundamental C programming concepts.

An introduction to the case control structure and how it is implemented using a switch within the C++ programming language.

Students work in small collaborative design teams to propose, build, and document a semester-long project focused on mobile applications for cell phones. Additional assignments include creating several small mobile applications such as context-aware mobile media capture and games. Students document their work through a series of written and oral proposals, progress reports, and final reports. This course covers the basics of J2ME and explores mobile imaging and media creation, GPS location, user-centered design, usability testing, and prototyping. Java experience is recommended.

The Composite Design Pattern is an object-oriented representation of a recursive data structure.

Introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, and processes) and their mechanization using lower-level elements. Analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems. Instruction set design issues; architectural support for contemporary software structures.

" 6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops a series of building blocks — logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples. 6.004 is required material for any EECS undergraduate who wants to understand (and ultimately design) digital systems. A good grasp of the material is essential for later courses in digital design, computer architecture and systems. The problem sets and lab exercises are intended to give students "hands-on" experience in designing digital systems; each student completes a gate-level design for a reduced instruction set computer (RISC) processor during the semester."

16.225 is a graduate level course on Computational Mechanics of Materials. The primary focus of this course is on the teaching of state-of-the-art numerical methods for the analysis of the nonlinear continuum response of materials. The range of material behavior considered in this course will include: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms will include: Variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There will be a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. At the beginning of the course, the students will be given the source of a base code with all the elements of a finite element program which constitute overhead and do not contribute to the learning objectives of this course (assembly and equation-solving methods, etc.). Each assignment will consist of formulating and implementing on this basic platform, the increasingly complex algorithms resulting from the theory given in class, as well as in using the code to numerically solve specific problems. The application to real engineering applications and problems in engineering science will be stressed throughout.

Project-based subject in which students from multiple disciplines are encouraged to develop and investigate systems and ideas from their fields of study as they explore the process of building and testing models and simulations. Explores various modeling software packages, criteria for developing the most appropriate simulation for a given situation, and methods for evaluating the success and utility of models. Students with an education focus consider what and how people learn from simulations, and how modeling tools can be implemented in public school settings. Graduate students are expected to complete additional assignments. During the past ten years, simulation modeling, especially as it helps people to understand complex systems, has become a mainstream use of computational technology. The widespread popularity of "edutainment" software like SimCity and Civilization gives a clear indication of the extent to which simulation games have permeated popular culture. As these and other games have found places in the classroom, researchers have tried to ascertain what and how students learn from these environments, and what implications this has for software and curriculum design. While it can be useful to experiment with pre-built models like SimCity, a deeper understanding can come through building and manipulating models whose underlying structure is accessible. Just as a young child learns more by building a bridge out of blocks instead of merely playing with a pre-fabricated bridge, designing and creating your own models provide richer learning experiences than simply playing with pre-built models. This learning process is critically important in domains that require an understanding of complex systems, from economics and mathematics to physics and biology. In this project-based course, students from all disciplines are encouraged to understand how we learn from computer environments, develop and investigate systems and ideas from their fields of study, and delve into the process of building and testing models and simulations. In the first part of the course we will explore the design and use of games and simulations in the classroom, and the research and development issues associated with this software. We will then explore various modeling and simulation software packages, criteria for developing the most appropriate simulation for a given situation, and methods for evaluating the success and utility of models. We will also study what and how people learn from simulations (including field testing of software), and how modeling and simulation tools can be implemented in educational settings. All levels of computer experience welcome. Graduate students are expected to complete additional assignments.

Introduction to computer graphics hardware, algorithms, and software. Topics include: line generators, affine transformations, line and polygon clipping, splines, interactive techniques, perspective projection, solid modeling, hidden surface algorithms, lighting models, shading, and animation. Substantial programming experience required. 6.837 offers an introduction to computer graphics hardware, algorithms, and software. Topics include: line generators, affine transformations, line and polygon clipping, splines, interactive techniques, perspective projection, solid modeling, hidden surface algorithms, lighting models, shading, and animation. Substantial programming experience is required.

This lesson compares iterative and recursive factorial functions. [Computer Programming playlist: 13 of 23]

This lesson shows how to define a function in Python. [Computer Programming playlist: 10 of 23]

This lesson will help you learn about variable scope and function calls in Python. [Computer Programming playlist: 11 of 23]

This lesson is an introduction to the fibonacci Sequence and a programming challenge. [Computer Programming playlist: 14 of 23]

This lesson is a challenge to write a Python function that can sort in list in-place. [Computer Programming playlist: 19 of 23]

This lesson shows a flowchart for the factorial program. [Computer Programming playlist: 8 of 23]

This lesson discusses 'for loops' in Python. [Computer Programming playlist: 3 of 23]

This lesson shows how to experiment and see what can be done with strings in Python programming. [Computer Programming playlist: 5 of 23]