This course will provide a gentle, yet intense, introduction to programming using Python for highly motivated students with little or no prior experience in programming. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. The course is designed to help prepare students for 6.01 Introduction to EECS. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity--with Euclid's algorithm and other ancient examples of computational thinking--the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, GĚŚdel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and quantum computing and the physical limits of computation. Class participation is essential, as the class will include discussion and debate about the implications of many of these ideas.

6.976 covers system level issues of high speed communication systems and their impact on circuit requirements, with primary focus being placed on wireless and broadband data link applications. Course topics include: transistor level design techniques for high speed amplifiers, mixers, VCO's, registers and gates, and phase locked loops, and the impact of transmission line effects on circuit designs for narrowband and broadband systems. Finally, behavioral level simulation techniques are presented for phase locked loops and other communication circuits.

The single most important skill for a computer scientist is problem solving. The goal of this book is to teach you to think like a computer scientist.

Python is a fun and extremely easy-to-use programming language that has steadily gained in popularity over the last few years. Developed over ten years ago by Guido van Rossum, Python's simple syntax and overall feel is largely derived from ABC, a teaching language that was developed in the 1980's. However, Python was also created to solve real problems and it borrows a wide variety of features from programming languages such as C++, Java, Modula-3, and Scheme. Because of this, one of Python's most remarkable features is its broad appeal to professional software developers, scientists, researchers, artists, and educators. 278 page pdf file.

This course focuses on the fundamentals of information security that are used in protecting both the information present in computer storage as well as information traveling over computer networks. Upon successful completion of this course, the student will be able to: explain the challenges and scope of information security; explain such basic security concepts as confidentiality, integrity, and availability, which are used frequently in the field of information security; explain the importance of cryptographic algorithms used in information security in the context of the overall information technology (IT) industry; identify and explain symmetric algorithms for encryption-based security of information; identify and explain public key-based asymmetric algorithms for encryption-based security of information; describe the access control mechanism used for user authentication and authorization; describe Secure Sockets Layer (SSL) as a common solution enabling security of many applications, including all Internet-based commerce; describe securing Internet Protocol (IP) communications by using Internet Protocol Security (IPSec); explain the importance of physical security and discuss ways to improve physical security of an enterprise; explain the use of such security tools as firewalls and intrusion prevention systems; explain malicious software issues, such as those brought forth by software-based viruses and worms; explain common software security issues, such as buffer overflow; describe the basic process of risk assessment in the context of overall IT security management. (Computer Science 406)

The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.

This course is intended to assist undergraduates with learning the basics of programming in general and programming MATLAB in particular.

Designed as a freshmen seminar course, faculty from various School of Engineering departments describe the research and educational opportunities specific to and offered by their departments. Background lectures by the 20.010J staff introduce students to the fundamental scientific basis for bioengineering. Specially produced videos provide additional background information that is supplemented with readings from newspaper and magazine articles. Bioengineering at MIT is represented by the diverse curricula offered by most Departments in the School of Engineering. This course samples the wide variety of bioengineering options for students who plan to major in one of the undergraduate Engineering degree programs. The beginning lectures describe the science basis for bioengineering with particular emphasis on molecular cell biology and systems biology. Bioengineering faculty will then describe the bioengineering options in a particular engineering course as well as the type of research conducted by faculty in the department.

This is a fast-paced introductory course to the C++ programming language. It is intended for those with little programming background, though prior programming experience will make it easier, and those with previous experience will still learn C++-specific constructs and concepts. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Dear student! You are starting to learn about computation and its purpose. This course covers the same materials as an introductory class for undergraduate computer science majors. Its curriculum, which includes software, hardware and algorithms, resembles that of a one- or two-semester first-year college course or the high school Advanced Placement (AP) Computer Science. It does not require a formal computer science background.

This course will introduce students to the field of computer science and the fundamentals of computer programming. No prior programming experience is required. Upon successful completion of this course, students will be able to: Demonstrate an understanding of the history of computing as well as fundamental hardware and software concepts; Demonstrate an understanding of the programming life cycle; Explain how the JVM translates Java code into executable code; Demonstrate an understanding of Object-Oriented Programming concepts; Demonstrate an understanding of basic Java concepts by writing simple programs; Demonstrate an understanding of logical and relational operators as well as control structures; Demonstrate proficiency in basic Java I/O techniques by writing small programs. (Computer Science 101; See also: Mathematics 302)

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.

This course presents the fundamentals of object-oriented software design and development, computational methods and sensing for engineering, and scientific and managerial applications. It cover topics, including design of classes, inheritance, graphical user interfaces, numerical methods, streams, threads, sensors, and data structures. Students use Java programming language to complete weekly software assignments. How is 1.00 different from other intro programming courses offered at MIT? 1.00 is a first course in programming. It assumes no prior experience, and it focuses on the use of computation to solve problems in engineering, science and management. The audience for 1.00 is non-computer science majors. 1.00 does not focus on writing compilers or parsers or computing tools where the computer is the system; it focuses on engineering problems where the computer is part of the system, or is used to model a physical or logical system. 1.00 teaches the Java programming language, and it focuses on the design and development of object-oriented software for technical problems. 1.00 is taught in an active learning style. Lecture segments alternating with laboratory exercises are used in every class to allow students to put concepts into practice immediately; this teaching style generates questions and feedback, and allows the teaching staff and students to interact when concepts are first introduced to ensure that core ideas are understood. Like many MIT classes, 1.00 has weekly assignments, which are programs based on actual engineering, science or management applications. The weekly assignments build on the class material from the previous week, and require students to put the concepts taught in the small in-class labs into a larger program that uses multiple elements of Java together.

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software. Acknowledgements The course materials were developed jointly by Prof. Stephen Boyd (Stanford), who was a visiting professor at MIT when this course was taught, and Prof. Lieven Vanderberghe (UCLA).

An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course - bits, signals, and packets - cover three corresponding layers of abstraction that form the basis of communication systems like the Internet. The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels.

This course is an introductory subject in the field of electric power systems and electrical to mechanical energy conversion. Electric power has become increasingly important as a way of transmitting and transforming energy in industrial, military and transportation uses. Electric power systems are also at the heart of alternative energy systems, including wind and solar electric, geothermal and small scale hydroelectric generation.

This course is an introduction to linear optimization and its extensions emphasizing the underlying mathematical structures, geometrical ideas, algorithms and solutions of practical problems. The topics covered include: formulations, the geometry of linear optimization, duality theory, the simplex method, sensitivity analysis, robust optimization, large scale optimization network flows, solving problems with an exponential number of constraints and the ellipsoid method, interior point methods, semidefinite optimization, solving real world problems problems with computer software, discrete optimization formulations and algorithms.

This subject provides an introduction to modeling and simulation, covering continuum methods, atomistic and molecular simulation, and quantum mechanics. Hands-on training is provided in the fundamentals and applications of these methods to key engineering problems. The lectures provide exposure to areas of application based on the scientific exploitation of the power of computation. We use web based applets for simulations, thus extensive programming skills are not required.

This course provides a general overview of databases, including topics such as database history, modern database systems, the different models used to design a database, and Structured Query Language (SQL), which is the standard language used to access and manipulate databases. Upon successful completion of this course, the student will be able to: compare and contrast the database approach and the file system approach; explain what a database management system is as well as the various components; identify the various people involved in database management systems; explain the historical background of database management systems; compare the various database models; describe the functions of a database management system; explain the three-schema database architecture; describe what tables, indexes, and views are as well as discuss the differences among them; explain the entity-relationship model; develop an entity-relationship model based on user requirements; describe the relational database model; explain the process of normalization; convert an entity-relationship diagram to a set of normalized relations; explain referential integrity; identify how relational algebra is used to construct queries; describe and use data definition commands; describe and use data manipulation commands; explain how to join tables together for queries. (Computer Science 403)