Courses tagged with "Information control" (75)
Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world.
This course was recently revised to meet the MIT Undergraduate Communication Requirement (CR). It covers the same content as 18.310, but assignments are structured with an additional focus on writing.
This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.
Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.
Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example:
- The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science".
- A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions.
- Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget.
The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.
In this subject, we consider two basic topics in cellular biophysics, posed here as questions:
- Which molecules are transported across cellular membranes, and what are the mechanisms of transport? How do cells maintain their compositions, volume, and membrane potential?
- How are potentials generated across the membranes of cells? What do these potentials do?
Although the questions posed are fundamentally biological questions, the methods for answering these questions are inherently multidisciplinary. As we will see throughout the course, the role of mathematical models is to express concepts precisely enough that precise conclusions can be drawn. In connection with all the topics covered, we will consider both theory and experiment. For the student, the educational value of examining the interplay between theory and experiment transcends the value of the specific knowledge gained in the subject matter.
This course is jointly offered through four departments, available to both undergraduates and graduates.
In this subject, we consider two basic topics in cellular biophysics, posed here as questions:
- Which molecules are transported across cellular membranes, and what are the mechanisms of transport? How do cells maintain their compositions, volume, and membrane potential?
- How are potentials generated across the membranes of cells? What do these potentials do?
Although the questions posed are fundamentally biological questions, the methods for answering these questions are inherently multidisciplinary. As we will see throughout the course, the role of mathematical models is to express concepts precisely enough that precise conclusions can be drawn. In connection with all the topics covered, we will consider both theory and experiment. For the student, the educational value of examining the interplay between theory and experiment transcends the value of the specific knowledge gained in the subject matter.
This course is jointly offered through four departments, available to both undergraduates and graduates.
6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.
6.005 Software Construction introduces fundamental principles and techniques of software development, i.e., how to write software that is safe from bugs, easy to understand, and ready for change. The course includes problem sets and a final project. Important topics include specifications and invariants; testing; abstract data types; design patterns for object-oriented programming; concurrent programming and concurrency; and functional programming.
The 6.005 website homepage from Spring 2016, along with all course materials, is available to OpenCourseWare users.
6.171 is a course for students who already have some programming and software engineering experience. The goal is to give students some experience in dealing with those challenges that are unique to Internet applications, such as:
- concurrency;
- unpredictable load;
- security risks;
- opportunity for wide-area distributed computing;
- creating a reliable and stateful user experience on top of unreliable connections and stateless protocols;
- extreme requirements and absurd development schedules;
- requirements that change mid-way through a project, sometimes because of experience gained from testing with users;
- user demands for a multi-modal interface.
