Skip to main content.
Ashokkumar Patel

faculty

Ashokkumar Patel

Associate Teaching Professor

Computer & Information Science

Contact

508-999-9184

ashok.patel@umassd.edu

Dion 302B

Teaching

  • Advanced Machine Learning
  • Big Data Analytics
  • Ethical Hacking
  • Database Design
  • Network Security

Teaching

Programs

Teaching

Courses

Constructing computer programs that automatically improve with experience is the main task of machine learning. The key algorithms in the area are presented. Learning concepts as decision trees, artificial neural networks and Bayesian approach are discussed. The standard iterative dichotomizer (ID3) is presented, the issues of overfitting, attribute selection and handling missing data are discussed. Neural nets are discussed in detail, examples of supervised and unsupervised learning are presented. Instance-based learning, i.e. k-nearest neighbor learning, case-based reasoning are introduced. Genetic algorithms are discussed on introductory level.

The relational, hierarchical, and network approaches to database systems, including relational algebra and calculus, data dependencies, normal forms, data semantics, query optimization, and concurrency control on distributed database systems.

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.

      Back to top of screen