Ashokkumar Patel

faculty

Ashokkumar Patel

Assistant Teaching Professor

Computer & Information Science

508-999-9184

apatel38@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.

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.

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.  

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.

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.

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

Prerequisite: Completion of three core courses. Research leading to submission of a formal thesis. This course provides a thesis experience, which offers a student the opportunity to work on a comprehensive research topic in the area of computer science in a scientific manner. Topic to be agreed in consultation with a supervisor. A written thesis must be completed in accordance with the rules of the Graduate School and the College of Engineering. Graded A-F.

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.

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.

Preservation, identification, extraction and documentation of evidence in any computing environment. This course follows a practical approach to the practice of digital forensics while presenting technical and legal matters related to forensic investigations. It introduces various technologies used in everyday computing environments along with detailed information on how the evidence contained on these devices should be analyzed.

Offered as needed to present advanced material to graduate students.
Register for this course.

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.
Register for this course.

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.
Register for this course.

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.