J. Michael Steele

Past Courses

• CIS 899 - PHD INDEPENDENT STUDY

  For doctoral students studying a specific advanced subject area in computer and information science. The Independen t Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 500 or 600 level course. The Independent Study may also be used by doctoral students to explore research options with faculty, prior to determining a thesis topic. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc. The Independent Study should not be used for ongoing research towards a thesis, for which the CIS 999 designation should be used.

• EAS 499 - SENIOR CAPSTONE

  The Senior Capstone Project is required for all BAS degree students, in lieu of the senior design course. The Capstone Project provides an opportunity for the student to apply the theoretical ideas and tools learned from other courses. The project is usually applied, rather than theoretical, exercise, and should focus on a real world problem related to the career goals of the student. The one-semester project may be completed in either the fall or sprong term of the senior year, and must be done under the supervision of a sponsoring faculty member. To register for this course, the student must submit a detailed proposal, signed by the supervising professor, and the student's faculty advisor, to the Office of Academic Programs two weeks prior to the start of the term.

• ESE 099 - INDEPENDENT STUDY

  An opportunity for the student to become closely associated with a professor in (1) a research effort to develop research skills and technique and/or (2) to develop a program of independent in-depth study in a subject area in which the professor and student have a common interest. The challenge of the task undertaken must be consistent with the student's academic level. To register for this course, the student and professor jointly submit a detailed proposal to the undergraduate curriculum chairman no later than the end of the first week of the term.

• MATH546 - ADV APPLIED PROBABILITY

  The required background is (1) enough math background to understand proof techniques in real analysis (closed sets, uniform covergence, fourier series, etc.) and (2) some exposure to probability theory at an intuitive level (a course at the level of Ross's probability text or some exposure to probability in a statistics class). After a summary of the necessary results from measure theory, we will learn the probabilist's lexicon (random variables, independence, etc.). We will then study laws of large numbers, Central Limit Theorem, Poisson convergence and processes, conditional expectations, and martingales. Emphasis is on using these for probability modeling. Application areas include genetics, linguistics, machine learning, agent-based models, statistical physics, and hidden Markov models.

• STAT399 - INDEPENDENT STUDY

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT430 - PROBABILITY

  Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

• STAT433 - STOCHASTIC PROCESSES

  An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT510 - PROBABILITY

  Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals. A one-year course in calculus is recommended.

• STAT533 - STOCHASTIC PROCESSES

  An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

• STAT899 - INDEPENDENT STUDY

  Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

• STAT930 - PROBABILITY THEORY

  Measure theory and foundations of Probability theory. Zero-one Laws. Probability inequalities. Weak and strong laws of large numbers. Central limit theorems and the use of characteristic functions. Rates of convergence. Introduction to Martingales and random walk.

• STAT931 - STOCHASTIC PROCESSES

  Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independen increments. Brownian motion and the theory of weak convergence. Point processes.

• STAT955 - STOCH CAL & FIN APPL

  Selected topics in the theory of probability and stochastic processes.

• STAT991 - SEM IN ADV APPL OF STAT

  This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

• STAT995 - DISSERTATION

• STAT999 - INDEPENDENT STUDY

  Written permission of instructor and the department course coordinator required to enroll.

• Wharton Undergraduate Excellence in Teaching Award, 2010
• Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990
• Fellow, American Statistical Association, 1989
• Fellow, Institute for Mathematical Statistics, 1984

Knowledge @ Wharton

• Polling the Polling Experts: How Accurate and Useful Are Polls These Days?, Knowledge @ Wharton - 11/14/2007
• To Blog or Not to Blog: Report from the Front, Knowledge @ Wharton - 10/18/2006