# 5 Classes (60 Units)

**6.006**(12),

**6.03**(12),

**6.036**(12),

**17.835**(12),

**18.03**(12)

# 6.006 Introduction to Algorithms

Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

This class has 6.042, 6.0001, and 6.009 as prerequisites.

6.006 will be offered this semester (Spring 2019). It is instructed by J. Ku.

Lecture occurs 11:00 AM to 12:00 PM on Tuesdays and Thursdays in 26-100.

This class counts for a total of 12 credits.

You can find more information at the 6.006: Introduction to Algorithms - Massachusetts Institute of Technology site or on the 6.006 Stellar site.

# 6.03 Introduction to EECS via Medical Technology

Explores biomedical signals generated from electrocardiograms, glucose detectors or ultrasound images, and magnetic resonance images. Topics include physical characterization and modeling of systems in the time and frequency domains; analog and digital signals and noise; basic machine learning including decision trees, clustering, and classification; and introductory machine vision. Labs designed to strengthen background in signal processing and machine learning. Students design and run structured experiments, and develop and test procedures through further experimentation.

This class has 18.02, and 8.02 as prerequisites.

6.03 will be offered this semester (Spring 2019). It is instructed by C. M. Stultz and E. Adalsteinsson.

Lecture occurs 2:00 PM to 3:00 PM on Mondays and Wednesdays in 34-101.

This class counts for a total of 12 credits.

You can find more information at the MIT + 6.03 - Google Search site.

# 6.036 Introduction to Machine Learning

Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks. Students taking graduate version complete additional assignments. Meets with 6.862 when offered concurrently. Enrollment may be limited.

This class has 6.0001 as a prerequisite.

6.036 will not be offered this semester. It will be instructed by R. Barzilay, L. P. Kaelbling and T. Jaakkola.

Lecture occurs 9:30 AM to 11:00 AM on Tuesdays in 26-100.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.036&btnG=Google+Search&inurl=https site or on the 6.036 Stellar site.

# 17.835 Machine Learning and Data Science in Politics

Introduces students to politics by analyzing political science data sets with machine learning methodologies. Covers a variety of data science tools, including supervised and unsupervised learning methods, visualization techniques, text analysis, and network analysis. Emphasizes how the research methodologies can be used for studying political science. Topics include lobbying, international trade, political networks, and estimating ideologies of political leaders.

This class has 6.0001 as a prerequisite.

17.835 will be offered this semester (Spring 2019). It is instructed by I. S. Kim.

This class counts for a total of
12 credits.
This class counts as a **HASS S**.

You can find more information at the MIT + 17.835 - Google Search site or on the 17.835 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 (Spring 2019). It is instructed by D. Jerison.

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 MIT + 18.03 - Google Search site or on the 18.03 Stellar site.