Department of Statistics and Data Science
Course List for Fall 2018/Spring 2019

Revised: 7 August 2018 DRAFT -- TIMES and ROOMS approximate!
Courses whose numbers end with a are offered in the FALL. Courses whose numbers end with b are offered in the SPRING.
Courses whose numbers end with ab are offered both semesters. Courses with a gray background are not taught this year.

CourseNumberInstructorTimeRoom
Introduction to Statistics 101a-106a/501a-506a Jonathan Reuning-Scherer and Staff Tues, Thurs 1:00-2:15 OML 202
Data Exploration and Analysis 230a/530a/PLSC 530a Susan Wang Tues, Thurs 9:00-10:15 DL 220
(Bayesian) Probability and Statistics 238a/538a Joe Chang Tues, Thurs 1:00-2:15 ML 211
Probability Theory with Applications 241a/541a/MATH 241a Yihong Wu Mon, Wed 9:00-10:15 Davies Aud
Linear Models 312a/612a David Brinda Mon, Wed 11:35-12:50 WTS A60
Introduction to Causal Inference 314a Winston Lin Tues, Thurs 4:00-5:15 24 Hillhouse Rm 107
Applied Data Mining and Machine Learning 365a/565a John Lafferty and Derek Feng Tues, Thurs 9:00-10:15 WLH 201
Statistical Inference 410a/610a Zhou Fan Tues, Thurs 11:35-12:50 24 Hillhouse Rm 107
Optimization Techniques 430a/630a/ENAS 530a/EENG 437a/ECON 413a Sekhar Tatikonda Tues, Thurs 1:00-2:15 WLH 117
Statistical Case Studies 625a Susan Wang Mon, Wed 1:00-2:15 WTS A74
Computational Mathematics for Data Science 663a Roy Lederman TBD TBD
Information Theory Tools in Probability and Statistics 672a Andrew Barron TBD 24 Hillhouse Rm 107
High-Dimensional Statistical Estimation 679a Sahand Tues 2:30-5:00 pm 24 Hillhouse Rm 107
Statistical Methods in Neuroimaging 683a Dustin Scheinost and Joe Chang TBD 24 Hillhouse Room 107
Statistical Inference on Graphs 684a Yihong Wu Wed 2:30-5:00 pm 24 Hillhouse Rm 107
Spectral Graph Theory CPSC 662a Dan Spielman Mon, Wed 2:30-3:45 TBD
Individual Studies 480ab Staff - -
Practical Work 626b DGS - -
Statistical Consulting 627a/628b Derek Feng Fri 2:30-4:30 24 Hillhouse Rm 107
Independent Study or Topics Course 690ab DGS - -
Research Seminar in Probability 699ab Sekhar Tatikonda and David Pollard Fri 11:00-1:00 24 Hillhouse Rm 107
Departmental Seminar 700ab - Mon 4:15-5:30 24 Hillhouse Rm 107
Introductory Statistics 100b/500b David Brinda Mon, Wed, Fri 10:30-11:20 TBA
YaleData 123b Jessi Cisewski TBD TBD
YData: Lab Course 124b Jessi Cisewski TBD TBD
Intensive Introductory Statistics and Data Science 220b/520b Susan Wang Tues, Thurs 9:00-10:15 TBA
Data Exploration and Analysis 230b/530b/PLSC 530b Jonathan Reuning-Scherer Tues, Thurs 9:00-10:15 TBA
Theory of Statistics 242b/542b Andrew Barron Mon, Wed, Fri 9:25-10:15 TBA
Computational Tools for Data Science 262b/562b Sahand? Bracketed? TBD TBD
Stochastic Processes 351b/551b Yihong Wu and Sahand Mon, Wed 1:00-2:15 TBA
Data Analysis 361b/661b David Brinda Mon, Wed 2:30-3:45 TBA
Multivariate Statistics for Social Sciences 363b/563b Jonathan Reuning-Scherer Tues, Thurs 1:00-2:15 KRN 301
Information Theory 364b/664b Andrew Barron Tues, Thurs 11:35-12:50 24 Hillhouse Rm 107
Applied Data Mining and Machine Learning 365b/665b Derek Feng Mon, Wed 11:35-12:50 SCL 160
Advanced Probability 400b/600b/MATH 330b Sekhar Tatikonda Tues, Thurs 2:30-3:45 24 Hillhouse Rm 107
Senior Capstone: Statistical Case Studies 425b Susan Wang Tues, Thurs 11:35-12:50 TBA
Introduction (?) to Random Matrix Theory and Applications 615b Zhou Fan Tues Thur 1:00-2:15 pm 24 Hillhouse Rm 107
Statistical Methods in Computational Biology 645b Hongyu Zhao Thur 1:00-2:50 pm TBD
Statistical Learning Theory 669b Sahand Negahban Mon, Wed, 2:30-3:45 24 HH Room 107
Selected Topics in Neural Nets 671b Harrison Zhou Wed 9:00-11:30 (tentative) 24 Hillhouse Rm 107
Applied Spatial Statistics 674b/F&ES 781b Tim Gregoire Tues, Thurs 10:30-11:50 TBD
Design and Analysis of Algorithms CPSC 365b Daniel Spielman Tues, Thurs 2:30-3:45 DL 220
Research Design and Causal Inference PLSC 508b Winston Lin TBD TBD
An Introduction to R for Statistical Computing and Data Science (1/2 credit) 110a/510a - not taught this year-
Applied Linear Models 531a - not taught this year-
Probabilistic Networks, Algorithms, and Applications 667a - not taught this year-
Statistics and Data Science Computing Laboratory (1/2 credit) 110b/510b - not taught this year-
Nonparametric Estimation and Machine Learning 468b - not taught this year-
Senior Seminar and Project 490b - not taught this year-
Statistical Computing 662b - not taught this year-

Introductory Statistics (100b/500b)
Instructor: David Brinda
Time: Mon, Wed, Fri 10:30-11:20
Place: TBA
An introduction to statistical reasoning. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Application of statistical concepts to data; analysis of real-world problems. A faster-paced version of this course with a higher level of computing is being created: See STAT 220a.
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Introduction to Statistics (101a-106a/501a-506a)
Instructor: Jonathan Reuning-Scherer and Staff
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Webpage:  http://www.stat.yale.edu/Courses/QR/stat101106.html
A basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks of classes are attended by all students in STAT 101-106 together, as general concepts and methods of statistics are developed. The remaining weeks are divided into field-specific sections that develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence and only one may be taken for credit. No prerequisites beyond high school algebra. May not be taken after STAT 100 or 109.

Students enrolled in STAT 101-106 who wish to change to STAT 109, or those enrolled in STAT 109 who wish to change to STAT 101-106, must submit a course change notice, signed by the instructor, to their residential college dean by Friday, September 28. The approval of the Committee on Honors and Academic Standing is not required.
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Introduction to Statistics: Life Sciences (101a/E&EB 210aG/MCDB 215a)
Instructor: Jonathan Reuning-Scherer and Walter Jetz
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical and probabilistic analysis of biological problems presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics.
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Introduction to Statistics: Political Science (102a/EP&E 203a/PLSC 425a)
Instructor: Jonathan Reuning-Scherer and Kelly Rader
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical analysis of politics and quantitative assessments of public policies. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and health policy.
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Introduction to Statistics: Social Sciences (103a/SOCY 119a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research.
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Introduction to Statistics: Medicine (105a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical methods used in medicine and medical research. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data.
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[ Introduction to Statistics: Data Analysis (106a) ]

Introduction to Statistics: Fundamentals (109a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
General concepts and methods in statistics. Meets for the first half of the term only. May not be taken after STAT 100 or 101-106.
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[ An Introduction to R for Statistical Computing and Data Science (1/2 credit) (110a/510a) ]

[ Statistics and Data Science Computing Laboratory (1/2 credit) (110b/510b) ]

YaleData (123b)
Instructor: Jessi Cisewski
Time: TBD
Place: TBD
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YData: Lab Course (124b)
Instructor: Jessi Cisewski
Time: TBD
Place: TBD
Needed.
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Intensive Introductory Statistics and Data Science (220b/520b)
Instructor: Susan Wang
Time: Tues, Thurs 9:00-10:15
Place: TBA
An introductory statistics course with intensive computing, most likely for STEM students.
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Data Exploration and Analysis (230a/530a/PLSC 530a)
Instructor: Susan Wang
Time: Tues, Thurs 9:00-10:15
Place: DL 220
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used. After STAT 100 or the equivalent or with permission from the instructor; students without prior coursework in statistics should take STAT 100, 10X, or 200.
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Data Exploration and Analysis (230b/530b/PLSC 530b)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 9:00-10:15
Place: TBA
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used. After STAT 100 or the equivalent or with permission from the instructor; students from STAT 200 may be permitted in 230 but are encouraged to take 361 and/or 325.
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(Bayesian) Probability and Statistics (238a/538a)
Instructor: Joe Chang
Time: Tues, Thurs 1:00-2:15
Place: ML 211
Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used for calculations, simulations, and analysis of data.

Prerequisite: knowledge of single variable calculus is assumed. Some brief acquaintance with multivariable calculus (e.g. double integrals) and matrices would also be helpful but are not required.
Extra: STAT 238 Extra Session,  Tues 6:30-8:00,  24 Hillhouse Rm 107
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Probability Theory with Applications (241a/541a/MATH 241a)
Instructor: Yihong Wu
Time: Mon, Wed 9:00-10:15
Place: Davies Aud
Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling.
Extra: STAT 241 TA Session,  Thurs 6:30-7:30,  24 Hillhouse Rm 107
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Theory of Statistics (242b/542b)
Instructor: Andrew Barron
Time: Mon, Wed, Fri 9:25-10:15
Place: TBA
Study of the principles of statistical analysis. Topics include maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Some statistical computing.
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Computational Tools for Data Science (262b/562b)
Instructor: Sahand? Bracketed?
Time: TBD
Place: TBD
Assumes math chops and some type of programming.
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Linear Models (312a/612a)
Instructor: David Brinda
Time: Mon, Wed 11:35-12:50
Place: WTS A60
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms, with particular reference to the R statistical language.

After STAT 242 and MATH 222 or 225.

No final exam.
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Introduction to Causal Inference (314a)
Instructor: Winston Lin
Time: Tues, Thurs 4:00-5:15
Place: 24 Hillhouse Rm 107
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Stochastic Processes (351b/551b)
Instructor: Yihong Wu and Sahand
Time:  Mon, Wed 1:00-2:15
Place: TBA
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications chosen from image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution.
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Data Analysis (361b/661b)
Instructor: David Brinda
Time: Mon, Wed 2:30-3:45
Place: TBA
Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering.

After or concurrently with STAT 242 and MATH 222 or 225, or equivalents.
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Multivariate Statistics for Social Sciences (363b/563b)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: KRN 301
Introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics include principal components analysis, factor analysis, cluster analysis (hierarchical clustering, k-means), discriminant analysis, multidimensional scaling, and structural equations modeling. Extensive computer work using either SAS or SPSS programming software.

Prerequisites: knowledge of basic inferential procedures and experience with linear models.
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Information Theory (364b/664b)
Instructor: Andrew Barron
Time: Tues, Thurs 11:35-12:50
Place: 24 Hillhouse Rm 107
Foundations of information theory in mathematical communications, statistical inference, statistical mechanics, probability, and algorithmic complexity. Quantities of information and their properties: entropy, conditional entropy, divergence, redundancy, mutual information, channel capacity. Basic theorems of data compression, data summarization, and channel coding. Applications in statistics and finance. After Statistics 241.
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Applied Data Mining and Machine Learning (365a/565a)
Instructor: John Lafferty and Derek Feng
Time: Tues, Thurs 9:00-10:15
Place: WLH 201
Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.
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Applied Data Mining and Machine Learning (365b/665b)
Instructor: Derek Feng
Time: Mon, Wed 11:35-12:50
Place: SCL 160
Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.
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Advanced Probability (400b/600b/MATH 330b)
Instructor: Sekhar Tatikonda
Time: Tues, Thurs 2:30-3:45
Place: 24 Hillhouse Rm 107
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.
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Statistical Inference (410a/610a)
Instructor: Zhou Fan
Time: Tues, Thurs 11:35-12:50
Place: 24 Hillhouse Rm 107
A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. An introduction to statistical decision theory. Undergraduate probability at the level of Statistics 241a assumed.
Extra: STAT 610 Extra Session,  Fri 10:30-11:45,  24 Hillhouse Rm 107
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Senior Capstone: Statistical Case Studies (425b)
Instructor: Susan Wang
Time: Tues, Thurs 11:35-12:50
Place: TBA
Webpage:  https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. This is a senior seminar of limited size, but other students may join if space permits. A final project is required. S&DS or Applied Math majors who previously took Statistical Case Studies are not permitted to take this course.
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Optimization Techniques (430a/630a/ENAS 530a/EENG 437a/ECON 413a)
Instructor: Sekhar Tatikonda
Time: Tues, Thurs 1:00-2:15
Place: WLH 117
Fundamental theory and algorithms of optimization, emphasizing convex optimization. The geometry of convex sets, basic convex analysis, the principle of optimality, duality. Numerical algorithms: steepest descent, Newton's method, interior point methods, dynamic programming, unimodal search. Applications from engineering and the sciences.
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[ Nonparametric Estimation and Machine Learning (468b) ]

Individual Studies (480ab)
Instructor: Staff
Time: -
Place: -
Directed individual study for qualified students who wish to investigate an area of statistics not covered in regular courses. A student must be sponsored by a faculty member who sets the requirements and meets regularly with the student. Enrollment requires a written plan of study approved by the faculty adviser and the director of undergraduate studies.

Permission required. No final Exam.
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[ Senior Seminar and Project (490b) ]

[ Applied Linear Models (531a) ]

Introduction (?) to Random Matrix Theory and Applications (615b)
Instructor: Zhou Fan
Time: Tues Thur 1:00-2:15 pm
Place: 24 Hillhouse Rm 107
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Statistical Case Studies (625a)
Instructor: Susan Wang
Time: Mon, Wed 1:00-2:15
Place: WTS A74
Webpage:  https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. Limited size, with permission from the instructor required. STARRED? STAT 425 is a senior capstone version of this course that include a final project. Can both be taken? Probably not.
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Practical Work (626b)
Instructor: DGS
Time: -
Place: -
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician. This course is a one-credit requirement for the Ph.D. degree.
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Statistical Consulting (627a/628b)
Instructor: Derek Feng
Time: Fri 2:30-4:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~jay/627.html
Statistical consulting and collaborative research projects often require statisticians to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their expertise in probability, statistics, and data analysis. Students complete the course with individual projects supervised jointly by faculty outside the department and by one of the instructors. Students enroll for both terms and receive one credit at the end of the year.
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Statistical Methods in Computational Biology (645b)
Instructor: Hongyu Zhao
Time: Thur 1:00-2:50 pm
Place: TBD
Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics; stochastic modeling and statistical methods applied to problems such as mapping disease-associated genes, analyzing gene expression microarray data, sequence alignment, and SNP analysis. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and some methods of classification and clustering; models include hidden Markov models, Bayesian networks, and the coalescent. The limitations of current models, and the future opportunities for model building, are critically addressed. Prerequisite: STAT 661a, 538a, or 542b. Prior knowledge of biology is not required, but some interest in the subject and a willingness to carry out calculations using R is assumed.
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[ Statistical Computing (662b) ]

Computational Mathematics for Data Science (663a)
Instructor: Roy Lederman
Time: TBD
Place: TBD
The course explores the mechanics of the interface between mathematics, computation and statistics in data analysis. We will discuss topics in numerical computation, complexity, programming and prototyping. Assignments will include theory, programming, data analysis, individual work, collaborative work and making mistakes.

Prerequisites: Linear algebra and some experience with programming (any language).
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[ Probabilistic Networks, Algorithms, and Applications (667a) ]

Statistical Learning Theory (669b)
Instructor: Sahand Negahban
Time: Mon, Wed, 2:30-3:45
Place: 24 HH Room 107
Introduction to theoretical analysis of machine learning algorithms. Focus on the statistical and computational aspects. Will cover subjects such as decision theory, empirical process theory, and convex optimization. Prerequisites linear algebra, multivariable calculus, stochastic processes, and introduction to machine learning such as Stat 365b or a similar course.
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Selected Topics in Neural Nets (671b)
Instructor: Harrison Zhou
Time: Wed 9:00-11:30 (tentative)
Place: 24 Hillhouse Rm 107
This is a graduate seminar course on some recent theoretical developments in neural nets. List of topics will include: 1) Nonconvex optimization. 2) Generalization theory. 3) Overparameterization. 4) GAN and VAE. 5) Mean filed view. 6) Implicit regularization. 7) Geometry. 8) Statistical theory.
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Information Theory Tools in Probability and Statistics (672a)
Instructor: Andrew Barron
Time: TBD
Place: 24 Hillhouse Rm 107
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Applied Spatial Statistics (674b/F&ES 781b)
Instructor: Tim Gregoire
Time: Tues, Thurs 10:30-11:50
Place: TBD
An introduction to spatial statistical techniques with computer applications. Topics include spatial sampling, visualizing spatial data, quantifying spatial association and autocorrelation, interpolation methods, fitting variograms, kriging, and related modeling techniques for spatially correlated data. Examples are drawn from ecology, sociology, public health, and subjects proposed by students. Four to five lab/homework assignments and a final project. The class makes extensive use of the R programming language as well as ArcGIS.
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High-Dimensional Statistical Estimation (679a)
Instructor: Sahand
Time: Tues 2:30-5:00 pm
Place: 24 Hillhouse Rm 107
In this course we will review the recent advances in high-dimensional statistics. We will cover concepts in empirical process theory, concentration of measure, and random matrix theory in the context of understanding the statistical properties of high-dimensional estimation methods. In this discussion we will also overview the computational constraints that are involved with solving high-dimensional problems and touch upon concepts in convex optimization and online learning.
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High-Dimensional Function Estimation (682a)
Instructor: Andrew Barron
Time: Mon, Wed 9:00-10:15
Place: 24 Hillhouse Room 107
Modern developments of high-dimensional function estimation, building from classical one-dimensional ingredients. Theory and methods for approximation, estimation, and computation. The blessing and the curse of high-dimensionality. Piece-wise polynomial, sinusoidal, and sigmoidal (artificial neural network) models. Product and ridge-basis models. Selection criteria. Deterministic and stochastic optimization strategies, including gradient methods, greedy algorithms, annealing and the associated theory of evolution of the parameters of the function estimates. Students will be responsible for a literature-based theory project/presentation and a computational project/presentation.
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Statistical Methods in Neuroimaging (683a)
Instructor: Dustin Scheinost and Joe Chang
Time: TBD
Place: 24 Hillhouse Room 107
Needed.
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Statistical Inference on Graphs (684a)
Instructor: Yihong Wu
Time: Wed 2:30-5:00 pm
Place: 24 Hillhouse Rm 107
An emerging research thread in statistics and machine learning deals with finding latent structures from data represented in graphs or matrices. This course will provide an introduction to mathematical and algorithmic tools for studying such problems. We will discuss information-theoretic methods for determining the fundamental limits, as well as methodologies for attaining these limits, including spectral methods, semidefinite programming relaxations, message passing algorithms, etc. Specific topics will include spectral clustering, planted clique and partition problem, sparse PCA, community detection on stochastic block models, statistical-computational tradeoffs.
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Independent Study or Topics Course (690ab)
Instructor: DGS
Time: -
Place: -
By arrangement with faculty. Approval of Director of Graduate Studies required.
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Research Seminar in Probability (699ab)
Instructor: Sekhar Tatikonda and David Pollard
Time: Fri 11:00-1:00
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~ypng
Continuation of the Yale Probability Group Seminar. Student and faculty explanations of current research in areas such as random graph theory, spectral graph theory, Markov chains on graphs, and the objective method.

Credit only with the explicit permission of the seminar organizers.
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Departmental Seminar (700ab)
Instructor: -
Time: Mon 4:15-5:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/Seminars/2011-12/
Important activity for all members of the department. See webpage for weekly seminar announcements.
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Design and Analysis of Algorithms (CPSC 365b)
Instructor: Daniel Spielman
Time: Tues, Thurs 2:30-3:45
Place: DL 220
Paradigms for problem solving: divide and conquer, recursion, greedy algorithms, dynamic programming, randomized and probabilistic algorithms. Techniques for analyzing the efficiency of algorithms and designing efficient algorithms and data structures. Algorithms for graph theoretic problems, network flows, and numerical linear algebra. Provides algorithmic background essential to further study of computer science. After CPSC 202 and 223.
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Spectral Graph Theory (CPSC 662a)
Instructor: Dan Spielman
Time: Mon, Wed 2:30-3:45
Place: TBD
An applied approach to spectral graph theory. The combinatorial meaning of the eigenvalues and eigenvectors of matrices associated with graphs. Applications to optimization, numerical linear algebra, error-correcting codes, computational biology, and the discovery of graph structure.
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Research Design and Causal Inference (PLSC 508b)
Instructor: Winston Lin
Time: TBD
Place: TBD
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