### Courses Taught

**6.867 Machine Learning **

Graduate (Fall)

Prereq: 6.041B or 18.600; 18.06

Units: 3-0-9

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Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, and Bayesian networks. Recommended prerequisite: 6.036.

*D. Shah*

**6.207 / 14.15 [J] Networks **

Undergrad (Spring) HASS Social Sciences

(Same subject as 14.15[J])

Prereq: 6.041B or 14.30

Units: 4-0-8

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Highlights common principles that permeate the functioning of diverse technological, economic and social networks. Utilizes three sets of tools for analyzing networks–random graph models, optimization, and game theory–to study informational and learning cascades; economic and financial networks; social influence networks; formation of social groups; communication networks and the Internet; consensus and gossiping; spread and control of epidemics; control and use of energy networks; and biological networks.

*D.Shah*

**6.438 Algorithms for Inference **

Graduate (Fall)

Prereq: 6.008, 6.041B, or 6.436; 18.06

Units: 4-0-8

Add to schedule Lecture: TR 9.30-11 (4-370) Recitation: F10 (8-119) or F11 (8-119) +final

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Introduction to statistical inference with probabilistic graphical models. Directed and undirected graphical models, and factor graphs, over discrete and Gaussian distributions; hidden Markov models, linear dynamical systems. Sum-product and junction tree algorithms; forward-backward algorithm, Kalman filtering and smoothing. Min-sum and Viterbi algorithms. Variational methods, mean-field theory, and loopy belief propagation. Particle methods and filtering. Building graphical models from data, including parameter estimation and structure learning; Baum-Welch and Chow-Liu algorithms. Selected special topics.

*P. Golland, G. W. Wornell, D. Shah*

**6.265 / 15.070 [J] Advanced Stochastic Processes **

Graduate (Fall) Can be repeated for credit

Prereq: 6.431, 15.085J, 18.100

Units: 4-0-8

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Analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems; elements of large deviations theory; Brownian motion and reflected Brownian motion; stochastic integration and Ito calculus; functional limit theorems. Applications to finance theory, insurance, queueing and inventory models.

*D. Gamarnik, D. Shah*

**6.266 Network Algorithms**

Prereq: 6.436

Units: 4-0-8

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Modern theory of networks from the algorithmic perspective with emphasis on the foundations in terms of modeling, performance analysis, and design. Topics include algorithmic questions arising in the context of scheduling, routing and congestion control in a communication network; information processing and data fusion in peer-to-peer, sensor and social networks; and efficient data storage/retrieval in a distributed storage network.

*D.Shah*

**6.454 Graduate Seminar in Area I **

Not offered academic year 2017-2018 Graduate (Fall) Can be repeated for credit

Prereq: Permission of instructor

Units: 2-0-4

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Student-run advanced graduate seminar with focus on topics in communications, control, signal processing, optimization. Participants give presentations outside of their own research to expose colleagues to topics not covered in the usual curriculum. Recent topics have included compressed sensing, MDL principle, communication complexity, linear programming decoding, biology in EECS, distributed hypothesis testing, algorithms for random satisfaction problems, and cryptogaphy. Open to advanced students from all areas of EECS. Limited to 12.

*L. Zheng, D. Shah*

**15.098 Seminar in Applied Probability and Stochastic Processes **

Graduate (Fall) Can be repeated for credit

Prereq: 6.431B

Units: 2-0-4

Sloan bid You must pre-register and participate in Sloan’s Course Bidding to take this subject.

Add to schedule TBA.

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Doctoral student seminar covering current topics in applied probability and stochastic processes.

*D. Gamarnik, D. Shah*