Online courses directory (10358)

18.311 Principles of Continuum Applied Mathematics covers fundamental concepts in continuous applied mathematics, including applications from traffic flow, fluids, elasticity, granular flows, etc. The class also covers continuum limit; conservation laws, quasi-equilibrium; kinematic waves; characteristics, simple waves, shocks; diffusion (linear and nonlinear); numerical solution of wave equations; finite differences, consistency, stability; discrete and fast Fourier transforms; spectral methods; transforms and series (Fourier, Laplace). Additional topics may include sonic booms, Mach cone, caustics, lattices, dispersion, and group velocity.

This course introduces the design of feedback control systems as applied to a variety of air and spacecraft systems. Topics include the properties and advantages of feedback systems, time-domain and frequency-domain performance measures, stability and degree of stability, the Root locus method, Nyquist criterion, frequency-domain design, and state space methods.

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.

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.

Principles of Biochemistry integrates an introduction to the structure of macromolecules and a biochemical approach to cellular function. Topics addressing protein function will include enzyme kinetics, the characterization of major metabolic pathways and their interconnection into tightly regulated networks, and the manipulation of enzymes and pathways with mutations or drugs. An exploration of simple cells (red blood cells) to more complex tissues (muscle and liver) will be used as a framework to discuss the progression in metabolic complexity. Learners will also develop problem solving and analytical skills that are more generally applicable to the life sciences.

New in version 2, learners will explore how alteration of these metabolic pathways relates to the development and progression of some human diseases.

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Image credit: PDB ID 2DN1, 2DN2

Park S-Y, Yokoyama T, Shibayama N, Shiro Y, Tame JRH (2006) 1.25 A resolution crystal structures of human haemoglobin in the oxy, deoxy and carbonmonoxy forms. J Mol Biol 360: 690–701.

5.112 is an introductory chemistry course for students with an unusually strong background in chemistry. Knowledge of calculus equivalent to MIT course 18.01 is recommended. Emphasis is on basic principles of atomic and molecular electronic structure, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis. The course also covers applications of basic principles to problems in metal coordination chemistry, organic chemistry, and biological chemistry.

This course provides an introduction to the chemistry of biological, inorganic, and organic molecules. The emphasis is on basic principles of atomic and molecular electronic structure, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis.

In an effort to illuminate connections between chemistry and biology, a list of the biology-, medicine-, and MIT research-related examples used in 5.111 is provided in Biology-Related Examples.

Acknowledgements

Development and implementation of the biology-related materials in this course were funded through an HHMI Professors grant to Prof. Catherine L. Drennan.

This course provides an introduction to the chemistry of biological, inorganic, and organic molecules. The emphasis is on basic principles of atomic and molecular electronic structure, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis. One year of high school chemistry is the expected background for this freshman-level course.

The aims include developing a unified and intuitive view of how electronic structure controls the three-dimensional shape of molecules, the physical and chemical properties of molecules in gases, liquids and solids, and ultimately the assembly of macromolecules as in polymers and DNA. Relationships between chemistry and other fundamental sciences such as biology and physics are emphasized, as are the relationships between the science of chemistry to its applications in environmental science, atmospheric chemistry and electronic devices. 

Acknowledgements

Professor Drennan would like to acknowledge the contributions of MIT Lecturer Dr. Elizabeth Vogel Taylor, Professor Sylvia Ceyer, and Professor Robert Silbey to the development of this course and its materials.

6.826 provides an introduction to the basic principles of computer systems, with emphasis on the use of rigorous techniques as an aid to understanding and building modern computing systems. Particular attention is paid to concurrent and distributed systems. Topics covered include: specification and verification, concurrent algorithms, synchronization, naming, networking, replication techniques (including distributed cache management), and principles and algorithms for achieving reliability.

This course introduces the basic mathematical and programming principles that underlie much of Computer Science. Students will refine their programming skills as well as learn the basics of creating efficient solutions to common computational problems.

This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Students will refine their programming skills as well as learn the basics of creating efficient solutions to common computational problems.

This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Students will refine their programming skills as well as learn the basics of creating efficient solutions to common computational problems.

This course provides an introduction to Python and elementary principles of computing, including iteration, recursion, and binary representation of data. Additional topics on cellular automata, encryption, and the limits of computation are also introduced. The goal of this course is to introduce some of the techniques used in computer science to solve complex problems, with or without a computer. The Python modules are used to introduce and illustrate conditionals and iteration; additional topics are provided without using a specific programming language and can be incorporated into classes that do not use Python.

This course deals with advanced design theories and textual analysis. Emphasis is placed on script analysis in general, as well as the investigation of design principles from a designer's perspective. Students also refine technical skills in rendering and presentation, historical research, and analysis. Class sessions include interaction with student/faculty directors and other staff designers. The goal of this course is for students to approach text with a fresh vision and translate that vision into design for performance.

In this UX course, we will survey theories and findings from the social sciences (especially psychology); with special attention to how these concepts influence the way we design for human interaction. We will cover how people perceive and process information, motor capabilities and limitations, decision-making and problem solving, and how emotion and social factors impact user experience. We will also cover best practices for designing systems to align with how people work.

This course is part of the User Experience (UX) Research and Design MicroMasters Program offered by MichiganX.

The course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451 Principles of Digital Communication II, is offered in the spring.

Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM modulation, signal constellations, finite-energy waveform spaces, detection, and modeling and system design for wireless communication.

This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm.

More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms; the BCJR algorithm; turbo codes, LDPC codes and RA codes; and performance of LDPC codes with iterative decoding. Finally, the course addresses coding for the bandwidth-limited regime, including lattice codes, trellis-coded modulation, multilevel coding and shaping. If time permits, it covers equalization of linear Gaussian channels.

This course is an introduction to discrete applied mathematics. Topics include probability, counting, linear programming, number-theoretic algorithms, sorting, data compression, and error-correcting codes. This is a Communication Intensive in the Major (CI-M) course, and thus includes a writing component.

Use economic analysis to reach conclusions about big issues of the day, from implications of globalization to causes of economic growth, financial crises and unemployment.

Use economic analysis to reach conclusions about big issues of the day, from implications of globalization to causes of economic growth, financial crises and unemployment.