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faculty
Ashokkumar Patel, PhD
Associate Teaching Professor
Computer & Information Science
Education
| 2002 | North Gujarat University | PhD |
Teaching
- Advanced Machine Learning
- Big Data Analytics
- Ethical Hacking
- Database Design
- Network Security
Programs
Programs
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.
Study under the supervision of a faculty member in an area covered in a regular course not currently being offered. Conditions and hours to be arranged.
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.
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.
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.
Online and Continuing Education 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.
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.
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.
Offered as needed to present advanced material to graduate students.
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.