Curriculum

This course reviews graph algorithms, key algorithmic techniques (such as dynamic programming, greedy approaches, dividing to sub-problems, randomization). Advanced data structures are studied, as well as approximation algorithms, P vs NP, and String searching and pattern matching. Effective proof writing for proving the correctness of algorithms and their time complexity will be a key outcome of this course.

Artificial Intelligence (AI) seeks to understand the mechanisms underlying thought and intelligent behavior, with a particular focus on their embodiment in machines. Core topics include the integrating perspective of intelligent agents and how such systems can engage in: search and problem solving; symbolic and probabilistic knowledge representation and reasoning; planning; and machine learning. The course introduces both basic concepts and algorithms and explores how to apply them in the construction of systems that can interact intelligently with complex environments.

In this course, students will systematically study the fundamental principles of computer system security, including authentication, access control, capability, security policies, sandbox, software vulnerabilities, and web security. Topics include system security analysis, access control and various security models, identification and authentication, protection against external and internal threats, network protocols and Internet security.

This course enables students to understand the inner workings of computer systems: how programs are executed, how data (information) is stored and manipulated, how hardware is managed, and how information is communicated between computer systems. The course covers computer architecture, operating systems principles and functions, and an introduction to computer networking and cloud computing. Students will learn systems and network programming in a language like C, C, or Java, in the Windows and/or Linux operating systems.

Computing technologies shape our personal, social, and political lives in increasingly complex and consequential ways. It is becoming increasingly clear, if it had not been clear before, that we must grapple with the ethical implications and consequences of algorithmic decision making and computing technology at large. With the advent of Machine Learning and Artificial Intelligence—which are increasingly becoming key elements of computing technology—and a simultaneous consolidation of power in the technology industry, commonly referred to as Big Tech, this is a perfect time to be studying the ethical, social, legal, and political issues inherent to computing in particular, and technology in general. 

• Technologies are born and shaped by the societies in which they are developed. Thus, grappling with the ethics of technologies is important not only for ultimately creating more moral technologies but a more moral society. A series of critical readings, reflections, and discussions guide the student through a thorough consideration of issues involved ranging from impacts and harms, to continuums or spectrums of concerns. A series of case studies and consideration of alternative actions develop a students critical reasoning skills and provide them with a mechanism for applying their developing ethical outlook in real life. Finally, a series of ethical approaches and perspectives are presented that show how the history of technology and the existing power dynamics need not be the future of technology.

• This is a key course in the Computer Science curriculum which brings home for the student how to apply principles of social justice, equity, and inclusion towards being effective computing professionals in the future. 

This course is a comprehensive study of the concepts and techniques of Deep Learning. Course content works through Deep Neural Networks through to attention and sequence-to-sequence models. This is an applied course using PyTorch or similar ML/AI library in Python.

Prerequisite Courses: Introduction to AI

This course provides an introduction to computer vision including the fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation, convolutional networks, image classification, segmentation, object detection, transformers, and 3D computer vision. Both applications of classical machine learning and deep learning to approach these problems are explored. The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to implement substantial projects that resemble contemporary approaches to computer vision.

Prerequisite Courses: Introduction to AI and Advanced Algorithms

This course surveys the principal difficulties of working with written language data, the fundamental techniques that are used in processing natural language, and the core applications of NLP technology. Topics covered in the course include language modeling, text classification, labeling sequential data (tagging), parsing, information extraction, question answering, machine translation, and semantics. The dominant paradigm in contemporary NLP uses supervised machine learning to train models based on either probability theory or deep neural networks. Both approaches are studied.

The objective of language processing when working with complex technical texts is to be able to make sense of information in a mixed structured/unstructured format. Information models to approach formalized technical language processing are explored. This course covers advanced text processing and machine learning algorithms and techniques for working with knowledge graphs and text data. This includes a wide range of algorithms for neural networks, machine learning, graph processing, text processing, and information retrieval with a focus of gaining insights into the knowledge stored in data. This an implementation-intensive research-oriented seminar, where a particular data science application will be developed by reading research publication and implementing a software prototype.

Prerequisite Courses: Introduction to AI and Advanced Algorithms 

Cryptography provides foundations for ensuring the confidentiality, authenticity, integrity and privacy of the increasing sensitive information in a digitally connected world. It is a theoretical field that relies on a diverse and wide variety of mathematics. This course includes topics such as encryption, message authentication codes, digital signatures, public key cryptographic systems, key exchange, identification protocols, zero-knowledge proof systems, etc., that are fundamental to secure communications on today's internet. The goal of this course is to help students develop a solid understanding of the fundamentals of secure communications and cryptography to prevent attacks on information in transit. 

Prerequisite Courses: Advanced Algorithms, Computer Systems and Networking and Information Security

Students in this course learn key aspects of Cybersecurity Incident Response Management (CIRM). Effective CIRM begins with effective Planning – including plans for Incident Response, Disaster Recovery, and Business Continuity.  Students in this course will learn from case studies of past cyber incidents. Key skills developed in this course include the developing of an Incident Response Plan, ethical and best practices on handling public/private communications and disclosures after an incident, incident investigation techniques (forensics) and interfacing with law enforcement, and post-incident recovery. Students in this course will learn how to plan for, respond to, investigate, and report on Cybersecurity Incidents. Before an incident happens, effective Cybersecurity Leadership requires that there is a cogent Governance structure. Students in this course will learn how to manage Cybersecurity processes while meeting the needs of the Enterprise. Students will learn how to establish a Governance program, Cybersecurity management frameworks, how to develop and implement a Cybersecurity strategy, how to develop and deploy Cybersecurity Policy and controls such that there is standards and regulatory compliance, techniques for advocating for the right organizational supports for Cybersecurity Leadership (ex: budgeting and training), how to effectively communicate with Executives and the Board, and how to acquire and develop talent for maintaining Cyber-resilience.

Prerequisite Course: Information Security

This course presents an overview of the principles and practices of digital investigation. The objective of this class is to emphasize the fundamentals and importance of digital forensics. Students will learn different techniques and procedures that enable them to perform a digital investigation. This course focuses mainly on the analysis of physical storage media and volume analysis. It covers the major phases of digital investigation such as preservation, analysis and acquisition of artifacts that reside in hard disks and random access memory. Students explore tools for the recovery of information on protected or damaged hardware for the purpose of providing evidence of misuse or abuse of systems. Topics also include the chain of evidence, protocols for data recovery, cryptographic analysis, password recovery, the bypassing of specific target operating systems, and obtaining data from digital devices that have been damaged or destroyed. Principles of defendant rights and ethical investigation are also discussed in detail.

Prerequisite Courses: Information Security, Computer Systems and Networking and Graduate Tech Ethics