Statistics Department
Course List for Fall 2018/Spring 2019

Revised: 26 March 2018 DRAFT PLEASE IGNORE!! PLACEHOLDER ONLY!!!
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
Monograph 8X1 Joe Chang TBD 24 Hillhouse Rm 107
Monograph 8X3 Yihong Wu TBD 24 Hillhouse Rm 107
Monograph 8X6 Andrew Barron TBD 24 Hillhouse Rm 107
Introduction to Statistics 101a-106a/501a-506a Jonathan Reuning-Scherer and Staff Tues, Thurs 1:00-2:15 OML 202
Introduction to Statistics (1/2 credit) 109a Jonathan Reuning-Scherer and Staff Tues, Thurs 1:00-2:15 OML 202
Data Exploration and Analysis S&DS 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 S&DS 241a/541a/MATH 241a Yihong Wu Mon, Wed 9:00-10:15 Davies Aud
Computational Tools for Data Science 262a/562a Roy or Elisa? Dan? Bracket? Tues, Thurs 2:30-3:45 DL 220
Linear Models 312a/612a Winston Lin Mon, Wed 11:35-12:50 WTS A60
Statistical Case Studies 625a Susan Wang Mon, Wed 1:00-2:15 WTS A74
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 S&DS 430a/630a/ENAS 530a/EENG 437a/ECON 413a Sekhar Tatikonda Tues, Thurs 1:00-2:15 WLH 117
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
Monograph: Causal Inference 8X2 Winston Lin TBD 24 Hillhouse Rm 107
Deep Learning? 8X4 Harrison Zhou TBD 24 Hillhouse Rm 107
Monograph 8X5 Zhou Fan TBD 24 Hillhouse Rm 107
Introductory Statistics 100b/500b David Brinda Mon, Wed, Fri 10:30-11:20 TBA
YaleData 123 Jessi Cisewski TBD TBD
YData: Lab Course 124 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 S&DS 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
Stochastic Processes 351b/551b Yihong Wu Mon, Wed 1:00-2:15 TBA
Advanced Probability S&DS 400b/600b/MATH 330b Sekhar Tatikonda Tues, Thurs 2:30-3:45 24 Hillhouse Rm 107
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
Senior Capstone: Statistical Case Studies 425b Susan Wang Tues, Thurs 11:35-12:50 TBA
Design and Analysis of Algorithms CPSC 365b Daniel Spielman Tues, Thurs 2:30-3:45 DL 220
An Introduction to R for Statistical Computing and Data Science (1/2 credit) 110a/510a - not taught this year-
Theory of Probability and Statistics 239a/539a - not taught this year-
Applied Linear Models 531a - not taught this year-
Probabilistic Networks, Algorithms, and Applications 667a - not taught this year-
Topological Data Analysis 675a - not taught this year-
Statistical Learning Theory 469b/669b - not taught this year-
Senior Seminar and Project 490b - not taught this year-
Empirical Processes 609b - not taught this year-
Selected Topics in Statistical Decision Theory 611b - not taught this year-
Experimental Design 613b - not taught this year-
Asymptotics 618b - not taught this year-
Statistical Methods in Genetics and Bioinformatics 645b - not taught this year-
Topics in Bayesian Inference and Data Analysis 654b - not taught this year-
Statistical Computing 662b - not taught this year-
Nonparametric Estimation and Machine Learning new course - not taught this year-
Applied Spatial Statistics 674b/F&ES 781b - not taught this year-

Monograph (8X1)
Instructor: Joe Chang
Time: TBD
Place: 24 Hillhouse Rm 107
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Monograph: Causal Inference (8X2)
Instructor: Winston Lin
Time: TBD
Place: 24 Hillhouse Rm 107
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Monograph (8X3)
Instructor: Yihong Wu
Time: TBD
Place: 24 Hillhouse Rm 107
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Deep Learning? (8X4)
Instructor: Harrison Zhou
Time: TBD
Place: 24 Hillhouse Rm 107
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Monograph (8X5)
Instructor: Zhou Fan
Time: TBD
Place: 24 Hillhouse Rm 107
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Monograph (8X6)
Instructor: Andrew Barron
Time: TBD
Place: 24 Hillhouse Rm 107
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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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YaleData (123)
Instructor: Jessi Cisewski
Time: TBD
Place: TBD
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YData: Lab Course (124)
Instructor: Jessi Cisewski
Time: TBD
Place: TBD
Needed.
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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 (1/2 credit) (109a)
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
This is a 1/2 credit option for completing the first part of the big STAT 103-106 course (see above). If you would like to take STAT 230 but never had any prior introductory statistics, you should consider this course.
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Introduction to Statistics: Life Sciences (S&DS 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 (S&DS 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 (S&DS 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)
Instructor: John Emerson
Time: Tues, Thurs 9:00-10:15
Place: TEAL
This is a 1/2 credit course that meets for the first 7 weeks of the semester. The class provides an introduction to the R statistical language, based on the S language developed at Bell Labs by John Chambers and Richard Becker. It has become the accepted language for advanced statistical computing and data sciencei in both industry and a wide range of academic disciplines. The course will conclude with either a final project using R or (for students who prefer) a very brief introduction to Python.
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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 (S&DS 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 (S&DS 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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[ Theory of Probability and Statistics (239a/539a) ]

Probability Theory with Applications (S&DS 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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Stochastic Processes (351b/551b)
Instructor: Yihong Wu
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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Computational Tools for Data Science (262a/562a)
Instructor: Roy or Elisa? Dan? Bracket?
Time: Tues, Thurs 2:30-3:45
Place: DL 220
Assumes math chops and some type of programming.
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[ Applied Linear Models (STAT 531a) ]

Linear Models (312a/612a)
Instructor: Winston Lin
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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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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Advanced Probability (S&DS 400b/600b/MATH 330b)
Instructor: Sekhar Tatikonda
Time: Tues, Thurs 2:30-3:45
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~pollard/Courses/600.spring2017/
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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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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[ Statistical Learning Theory (STAT 469b/669b) ]

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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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) ]

[ Empirical Processes (STAT 609b) ]

Statistical Inference (410a/610a)
Instructor: Zhou Fan
Time: Tues, Thurs 11:35-12:50
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~pollard/Courses/610.fall2014/
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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[ Selected Topics in Statistical Decision Theory (STAT 611b) ]

[ Experimental Design (STAT 613b) ]

[ Asymptotics (STAT 618b) ]

Practical Work (STAT 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 Genetics and Bioinformatics (645b) ]

[ Topics in Bayesian Inference and Data Analysis (STAT 654b) ]

[ Statistical Computing (STAT 662b) ]

[ Probabilistic Networks, Algorithms, and Applications (STAT 667a) ]

[ Nonparametric Estimation and Machine Learning (new course) ]

[ Applied Spatial Statistics (STAT 674b/F&ES 781b) ]

[ Topological Data Analysis (STAT 675a) ]

Independent Study or Topics Course (STAT 690ab)
Instructor: DGS
Time: -
Place: -
By arrangement with faculty. Approval of Director of Graduate Studies required.
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High-Dimensional Function Estimation (STAT 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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Research Seminar in Probability (STAT 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 (STAT 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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Optimization Techniques (S&DS 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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