# 5 Classes (60 Units)

**4.500**(12),

**6.004**(12),

**6.034**(12),

**6.042**(12),

**18.03**(12)

# 4.500 Introduction to Design Computing

Introduces 3-D CAD modeling to students with little or no experience in design or computation. Teaches surface, solid and mesh modeling techniques combined with a variety of modeling applications, from 3D printing to CNC fabrication and 3D rendering. Includes weekly modeling assignments leading up to a final project. Additional work required of students taking the graduate version. Enrollment limited; preference to Course 4 majors and minors.

This class has no prerequisites.

4.500 will be offered this semester (Fall 2017). It is instructed by L. Sass.

Lecture occurs 9:30 AM to 11:00 AM on Mondays and Wednesdays in 4-153.

This class counts for a total of 12 credits.

You can find more information at the MIT + 4.500 - Google Search site.

# 6.004 Computation Structures

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.

This class has no prerequisites.

6.004 will be offered this semester (Fall 2017). It is instructed by D. Sanchez, C. J. Terman and S. H. Wachman.

Lecture occurs 1:00 PM to 2:00 PM on Tuesdays and Thursdays in 10-250.

This class counts for a total of 12 credits.

You can find more information at the MIT + 6.004 - Google Search site.

# 6.034 Artificial Intelligence

Introduces representations, methods, and architectures used to build applications and to account for human intelligence from a computational point of view. Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals.

This class has 6.0001 as a prerequisite.

6.034 will be offered this semester (Fall 2017). It is instructed by P. H. Winston and K. Koile.

Lecture occurs 10:00 AM to 11:00 AM on Mondays, Wednesdays and Fridays in 10-250.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.034&btnG=Google+Search&inurl=https site.

# 6.042 Mathematics for Computer Science

Elementary discrete mathematics for computer science and engineering. Emphasis on mathematical definitions and proofs as well as on applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics include recursive definition and structural induction, state machines and invariants, integer congruences, recurrences, generating functions.

This class has 18.01 as a prerequisite.

6.042 will be offered this semester (Fall 2017). It is instructed by F. T. Leighton, A. R. Meyer and A. Moitra.

Lecture occurs 1:00 PM to 2:30 PM on Mondays, Wednesdays and Fridays in 32-044.

This class counts for a total of 12 credits.

You can find more information at the MIT + 6.042 - Google Search site or on the 6.042 Stellar site.

# 18.03 Differential Equations

Study of differential equations, including modeling physical systems. Solution of first-order ODEs by analytical, graphical, and numerical methods. Linear ODEs with constant coefficients. Complex numbers and exponentials. Inhomogeneous equations: polynomial, sinusoidal, and exponential inputs. Oscillations, damping, resonance. Fourier series. Matrices, eigenvalues, eigenvectors, diagonalization. First order linear systems: normal modes, matrix exponentials, variation of parameters. Heat equation, wave equation. Nonlinear autonomous systems: critical point analysis, phase plane diagrams.

This class has 18.02 as a corequisite.

18.03 will be offered this semester (Fall 2017). It is instructed by A. Negut.

Lecture occurs 1:00 PM to 2:00 PM on Mondays, Wednesdays and Fridays in 54-100.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+18.03&btnG=Google+Search&inurl=https site or on the 18.03 Stellar site.