Difference between revisions of "CDS course discussion, Apr 2014"

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=== CMS course requirements ===
 
=== CMS course requirements ===
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* Core requirements: 7 common courses taken by all CMS students (first year)
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* Depth requirement: 3 courses in a given area
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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.
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* Algorithmic economics: Auctions and mechanism design, algorithmic game theory, and privacy.
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* Biological circuits: Organic substrates for computation, including neuronal computing and DNA computing.
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* Feedback & control: Robust control, feedback, dynamical systems theory.
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* Inference & statistics: Statistical decision theory, information theory, and adaptive signal processing.
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* Information systems: Information theory, coding theory, communication, and signal processing.
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* Machine learning & vision: Algorithmic, mathematical, and biological perspectives on computational models for learning and vision.
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* Networked systems: The study of complex networks, in fields ranging from biology, social science, communications, and power.
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* Optimization: Convex optimization, conic and discrete optimization, and numerical methods for largescale optimization.
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* Quantum information theory: Quantum algorithms and complexity, convex optimization, and operator theory.
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* Scientific computing: Computational methods for problems arising in the physical sciences, partial differential equations.
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* Uncertainty quantification: Markov chains and martingales, stochastic system analysis, and convex optimization.
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Revision as of 17:20, 11 April 2014


CMS course requirements

  • 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
  • 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.
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)

Additional CMS courses

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