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faculty
Ashokkumar Patel, PhD
Associate Teaching Professor
Computer & Information Science
Contact
508-999-9184
ashok.patel@umassd.edu
Dion 302B
Education
| 2002 | North Gujarat University | PhD |
Teaching
- Advanced Machine Learning
- Big Data Analytics
- Ethical Hacking
- Database Design
- Network Security
Teaching
Programs
Programs
Teaching
Courses
Advanced coverage of machine learning algorithms and applications to computer vision, data mining and social media. Specifically, the course focuses on data pre-processing, feature extraction, supervised/unsupervised learning, graphical model, deep learning, and other advanced machine learning topics. The course will also explore several real-world problems, e.g., visual detection/recognition, topics discovery, social media analytics using machine learning approaches.
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.
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.
Offered as needed to present advanced material to graduate students.
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.
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.
Prerequisite: Graduate standing; approval by advisor, graduate program director and department chairperson. Experiential learning in conjunction with an industrial or governmental agency project under the joint supervision of an outside sponsor and a faculty advisor. To be eligible, a student should have completed at least half of his/her program of study. A detailed project proposal must be prepared by the student for departmental approval prior to the start of the project. Upon completion, student must submit a report on the experience and make a short presentation to his/her graduate committee. This course may be used to satisfy one 3-credit graduate technical elective course.
Teaching
Online and Continuing Education Courses
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.
Advanced coverage of machine learning algorithms and applications to computer vision, data mining and social media. Specifically, the course focuses on data pre-processing, feature extraction, supervised/unsupervised learning, graphical model, deep learning, and other advanced machine learning topics. The course will also explore several real-world problems, e.g., visual detection/recognition, topics discovery, social media analytics using machine learning approaches.
Offered as needed to present advanced material to graduate students.
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.
Vulnerabilities, attacks and current defenses and in-depth look at network security. Threats to computer networks through exploiting weaknesses in network design and protocols are analyzed and protection of data confidentiality, integrity and availability throughout the different network services are explored. Topics covered include cryptographic and authentication systems for data protection, network intrusion detection and forensics technologies, network security devices and access control mechanisms, communication privacy and anonymity, and new developments in Internet and Transport Protocols.
Register for this course.