8:45 pm E1 IND Duke-Cohan PSY T 6:00 pm 8:45 pm MET CS 580 Health Informatics Fall. Students who complete the programs prerequisites at Boston University can earn an undergraduate. PhDiZone, is an accredited preeminent research center to facilitate the PhD Scholars for their research process. MET CS 810 Master's Thesis in Computer Science Fall 18 This thesis must be completed within 12 months. Errors during measurements and computations are analyzed in the course. Implementation of polymorphism with inheritance and interfaces and in Java library containers. Students majoring in Computer Science may elect a thesis option. Prerequisite: MET CS 521 or MET CS 622 or MET CS 673. Certificate in Computer Science. Fall 2018 Section Type Instructor Location Days Times B1 IND Naidjate KCB 102 T 6:00 pm 8:45 pm Spring 2019 Section Type Instructor Location Days Times B1 IND Naidjate MCS T 6:00 pm 8:45 pm MET CS 673 Software Engineering Fall 18 Sprg 19 Techniques. Topics include mobile forensics procedures and principles, related legal issues, mobile platform internals, bypassing passcode, rooting or jailbreaking process, logical and physical acquisition, data recovery and analysis, and reporting. Iaas740 Comprehensive Issues.
The course will consider how these technologies solve mobility, routing, congestion, QoS (multi-media security, etc. Topics include secure software development processes, threat modeling, secure requirements and architectures, vulnerability and malware analysis using static code analysis and dynamic analysis tools, vulnerabilities in C/C and Java programs, Crypto and secure APIs, vulnerabilities in web applications and mobile applications, and security testing. Fall 2018 Section Type Instructor Location Days Times C1 IND Braude COM 215 W 6:00 pm 8:45 pm C2 IND Berry BRB 121 W 6:00 pm 8:45 pm O1 IND Braude ARR Spring 2019 Section Type Instructor Location Days Times C1 IND Braude CGS. Hierarchical graphics modeling is briefly studied.
It describes logical, physical and semantic foundation of modern DW infrastructure. The course covers a wide variety of approaches, including Supervised Learning, Neural Nets and Deep Learning, Reinforcement Learning, Expert Systems, Bayesian Learning, Fuzzy Rules, Genetic Algorithms, and Swarm Intelligence. A maximum of two graduate-level courses (8 credits) taken at Metropolitan College before acceptance into the program may be applied toward the degree. Fall 2018 Section Type Instructor Location Days Times B1 IND Yates BRB 122 T 6:00 pm 8:45 pm E1 IND Yates BRB 122 T 6:00 pm 8:45 pm Spring 2019 Section Type Instructor Location Days Times D1 IND Yates KCB R 6:00 pm 8:45. The emphasis is on those technologies that are either representative of a type or take a unique perspective on the problem. Prereq: MET CS 579 or MET CS 669; or instructor's consent. .