Ashokkumar Patel, PhD

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.

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.

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.

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.

Fundamental knowledge for capturing and analyzing large-scale data from diverse fields including human behavior, sensors, biological signals, and finance. The course introduces platforms for data storage systems and distributed processing of large datasets, covering big data pipeline concepts: collection, storage, ingestion, processing, analytics, and visualization. Students will work with platforms such as AWS Athena, AWS Glue, Kinesis, Elasticsearch, Kibana, Hadoop HDFS and MapReduce, and Spark.

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.

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.