MIT Subject Listing & Schedule
Fall 2024 Search Results

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11 subjects found.

1.050 Solid Mechanics

Introduction to statics and the principles of mechanics to describe the behavior of structures. Topics include free body diagrams, static equilibrium, force analysis of slender members, concept of stress and strain, linear elasticity, principal stresses and strains, Mohr's circle, and failure modes. Application to engineering structures such as bars, beams, frames, and trusses.

2.086 Numerical Computation for Mechanical Engineers

Covers elementary programming concepts, including variable types, data structures, and flow control. Provides an introduction to linear algebra and probability. Numerical methods relevant to MechE, including approximation (interpolation, least squares, and statistical regression), integration, solution of linear and nonlinear equations, and ordinary differential equations. Presents deterministic and probabilistic approaches. Uses examples from MechE, particularly from robotics, dynamics, and structural analysis. Assignments require MATLAB programming. Enrollment may be limited due to laboratory capacity; preference to Course 2 majors and minors.

5.12 Organic Chemistry I

Introduction to organic chemistry. Development of basic principles to understand the structure and reactivity of organic molecules. Emphasis on substitution and elimination reactions and chemistry of the carbonyl group. Introduction to the chemistry of aromatic compounds.

5.611 Introduction to Spectroscopy

Introductory quantum chemistry; particles and waves; wave mechanics; harmonic oscillator; applications to IR, Microwave and NMR spectroscopy. Combination of 5.611 and 5.612 counts as a REST subject.

5.612 Electronic Structure of Molecules

Introductory electronic structure; atomic structure and the Periodic Table; valence and molecular orbital theory; molecular structure, and photochemistry. Combination of 5.611 and 5.612 counts as a REST subject.

6.C06J Linear Algebra and Optimization

See description under subject 18.C06J.

7.03 Genetics

The principles of genetics with application to the study of biological function at the level of molecules, cells, and multicellular organisms, including humans. Structure and function of genes, chromosomes, and genomes. Biological variation resulting from recombination, mutation, and selection. Population genetics. Use of genetic methods to analyze protein function, gene regulation, and inherited disease.

8.286 The Early Universe

Introduction to modern cosmology. First half deals with the development of the big bang theory from 1915 to 1980, and latter half with recent impact of particle theory. Topics: special relativity and the Doppler effect, Newtonian cosmological models, introduction to non-Euclidean spaces, thermal radiation and early history of the universe, big bang nucleosynthesis, introduction to grand unified theories and other recent developments in particle theory, baryogenesis, the inflationary universe model, and the evolution of galactic structure.

18.06 Linear Algebra

Basic subject on matrix theory and linear algebra, emphasizing topics useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, singular value decomposition, and positive definite matrices. Applications to least-squares approximations, stability of differential equations, networks, Fourier transforms, and Markov processes. Uses linear algebra software. Compared with 18.700, more emphasis on matrix algorithms and many applications.

18.600 Probability and Random Variables

Probability spaces, random variables, distribution functions. Binomial, geometric, hypergeometric, Poisson distributions. Uniform, exponential, normal, gamma and beta distributions. Conditional probability, Bayes theorem, joint distributions. Chebyshev inequality, law of large numbers, and central limit theorem. Credit cannot also be received for 6.041A or 6.041B.

18.C06J Linear Algebra and Optimization

Introductory course in linear algebra and optimization, assuming no prior exposure to linear algebra and starting from the basics, including vectors, matrices, eigenvalues, singular values, and least squares. Covers the basics in optimization including convex optimization, linear/quadratic programming, gradient descent, and regularization, building on insights from linear algebra. Explores a variety of applications in science and engineering, where the tools developed give powerful ways to understand complex systems and also extract structure from data.