Shivani Agarwal

Shivani Agarwal
  • Rachleff Family Associate Professor
  • Associate Professor of Computer and Information Science
  • Associate Professor of Statistics and Data Science

Contact Information

  • office Address:

    3401 Walnut St 412B, Philadelphia, PA 19104

Links: Personal Website

Teaching

Current Courses

  • CIS8990 - Doctoral Independent Study

    For doctoral students studying a specific advanced subject area in computer and information science. The Independent Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 5000 or 6000 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 9990 designation should be used.

    CIS8990013

Past Courses

  • CIS5200 - Machine Learning

    This course covers the foundations of statistical machine learning. The focus is on probabilistic and statistical methods for prediction and clustering in high dimensions. Topics covered include linear and logistic regression, SVMs, PCA and dimensionality reduction, EM and HMMs, and deep learning. Elementary probability, calculus, and linear algebra. Basic programming experience.

  • CIS5970 - Master's Thesis Research

    For students working on an advanced research leading to the completion of a Master's thesis.

  • CIS5990 - Master's Indep Study

    For master's students studying a specific advanced subject area in computer and information science. Involves coursework and class presentations. A CIS 5990 course unit will invariably include formally gradable work comparable to that in a CIS 500-level course. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc.

  • CIS6200 - Adv Top in Mach Learning

    This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. The precise topics covered may vary from year to year based on student interest and developments in the field.

  • CIS7000 - Cis-Topics

    One time course offerings of special interest. Equivalent to a CIS 5XX level course.

  • CIS8000 - PhD Special Topics

    One-time course offerings of special interest. Equivalent to CIS seminar course. Offerings to be determined.

  • CIS8990 - PhD Independent Study

    For doctoral students studying a specific advanced subject area in computer and information science. The Independent Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 5000 or 6000 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 9990 designation should be used.

  • CIS9990 - Thesis/Diss Research

    For students pursuing advanced research to fulfill PhD dissertation requirements.

In the News

Knowledge @ Wharton

Activity

In the News

Former Levi’s CEO on Revitalizing a Brand — and the Right Way to Wash Jeans

Former Levi’s CEO Chip Bergh talks to Wharton’s Barbara Kahn and Americus Reed about reviving the iconic brand, purpose-driven marketing, and preserving your denim.…Read More

Knowledge @ Wharton - 2024/04/23
All News