CDS course discussion, Apr 2014: Difference between revisions

Revision as of 17:21, 11 April 2014

Core requirements: 7 common courses taken by all CMS students (first year)
Depth requirement: 3 courses in a given area
Breadth requirement: 3 courses from mathematics, science, engineering, or economics

Feedback & control: Robust control, feedback, dynamical systems theory.
Inference & statistics: Statistical decision theory, information theory, and adaptive signal processing.
Information systems: Information theory, coding theory, communication, and signal processing.
Networked systems: The study of complex networks, in fields ranging from biology, social science, communications, and power.
Optimization: Convex optimization, conic and discrete optimization, and numerical methods for largescale optimization.
Uncertainty quantification: Markov chains and martingales, stochastic system analysis, and convex optimization.

Track	Fall	Winter	Spring
Core	ACM 104. Linear algebra and applied operator theory ACM/EE 116. Introduction to stochastic processes and modeling ACM 113. Mathematical optimization network	CS/EE 144. Networks: Structure and economics ACM/EE 218. Statistical inference and learning signal	ACM 126. Mathematical signal processing CS 139. Theory of algorithms
Feedback and control		CDS 140a - Dynamical systems CDS 212 - Feedback control theory	CDS 140b - Dynamical systems (alt years) CDS 213 - Robust control CDS 270 - Advanced topics (nonlinear, adaptive, system ID)

Track	Fall	Winter	Spring
Mathematics	CDS 201 - Linear Algebra & Applied Operator Theory Jointly offered with ACM 104/AM 125a Vector spaces, including Banach and Hilbert spaces Linear operators, dual spaces, decompositions ~35 students, multiple options ACM 113 - Introduction to Optimization Convex analysis Linear programming/duality
Stochastic systems	Introduction to Stochastic Processes and Modeling ACM/EE 116 (Hassibi, Owhadi, Tropp) Probability spaces, conditional probability, random processes ~100 students/year from across the Institute	Markov Chains, Discrete Stochastic Processes and Applications ACM 216 (Owhadi, Tropp)	Stochastic Inference ACM 218 (Chandrasekaran) CDS 150 (Beck)
Dynamical Systems	CDS 140a -
Robust Control	Modern Control Theory CDS 212 (Doyle, Low, Murray) Dynamics and stability in discrete and continuous time Uncertainty and robustness Fundamental limits: Bode, Shannon, Bode/Shannon	Algorithmic Game Theory CS/Ec 241

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 * Breadth requirement: 3 courses from mathematics, science, engineering, or economics
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-* Algorithms & complexity: Approximation algorithms, online algorithms, complexity theory, and computability.
-* Algorithmic economics: Auctions and mechanism design, algorithmic game theory, and privacy.
-* Biological circuits: Organic substrates for computation, including neuronal computing and DNA computing.
 * Feedback & control: Robust control, feedback, dynamical systems theory.
 * Inference & statistics: Statistical decision theory, information theory, and adaptive signal processing.
 * Information systems: Information theory, coding theory, communication, and signal processing.
-* Machine learning & vision: Algorithmic, mathematical, and biological perspectives on computational models for learning and vision.
 * Networked systems: The study of complex networks, in fields ranging from biology, social science, communications, and power.
 * Optimization: Convex optimization, conic and discrete optimization, and numerical methods for largescale optimization.
-* Quantum information theory: Quantum algorithms and complexity, convex optimization, and operator theory.
-* Scientific computing: Computational methods for problems arising in the physical sciences, partial differential equations.
 * Uncertainty quantification: Markov chains and martingales, stochastic system analysis, and convex optimization.
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