Post Baccalaureate Diploma in Artificial Intelligence

The following courses are required for the program:

162 Programming and Data Structures  
Continuing from the material in CSCI 161, this course covers memory management and data abstraction via classes and objects, and introduces the linear data structures lists, stacks, and queues. Structured programming is encouraged via modular development. Credit will be granted for only one of CSCI 162 and INFO 256. Prerequisite: CSCI 125 or 161 or ENGR 147. Three credits and a two-hour lab.  

215 Social Issues in the Information Age  
This course exposes students to the various impacts of technology on modern society with the goal of further developing their critical thinking and their ability to make informed decisions in this rapidly changing information age. Topics covered include privacy and security, biotechnology, cybercrime, genetic engineering, artificial intelligence, digitization and intellectual property, ethical issues in computing. Other topics and/or their emphasis may vary by semester. Students from every background will benefit from this course. Three credits.  

223 Introduction to Data Science  
The course will provide students with the basic understanding of the theory and practice of data science and its applications in different real-world domains. Student will also gain practical skills in handling structured and unstructured data, analyzing and visualizing data, data mining, as well as gain hands-on experience of software tools and apply the basic techniques to their own different scientific, engineering and business applications. Prerequisite: One of CSCI 125, 128, 161 or 225. Three credits.  

225 Coding for Health Analytics  
Technological development has transformed modern healthcare. The large amounts of health data currently acquired and analyzed has the potential to positively affect a patient’s quality of life. This interdisciplinary course focuses on developing practical coding skills used in the healthcare domain, a rapidly growing field of computing that can have a beneficial impact on patient care and public health. Suitable for students from a variety of backgrounds planning a career involving health-related data. Open to students in all degree programs. Prerequisite: CSCI 128 or CSCI 125 or CSCI 161 or with permission of department chair. Three credits. 

255 Advanced Data Structures  
This course provides a deep investigation of foundational data structures and algorithms. Criteria for selecting appropriate data structures and algorithms for a given problem are presented. General problem solving is emphasized throughout the course. Specific topics include stacks, queues, lists, trees, searching, sorting, traversals, recursion, graphs, hashing, and complexity analysis. Prerequisite: CSCI 162. Three credits and a two-hour lab.  

263 Computer Organization  
This course covers basic computer arithmetic, architectures, and instruction sets; in-depth study of the central processing unit, memory and input/output organization; and microprogramming and interfacing. Credit will be granted for only one of CSCI 263 or INFO 225. Prerequisite: CSCI 162. Three credits and a two-hour lab.  

275 Database Management Systems  
An introduction to the theory and practice associated with the design and implementation of databases. Topics include database models (relational model in detail), design, normalization, transactions, SQL, and a DBMS (Oracle). Credit will be granted for only one of CSCI 275, BSAD 384 or INFO 275. Prerequisite: CSCI 162. Three credits and a two-hour lab.  

277 Discrete Structures  
An introduction to sets, binary relations and operations; induction and recursion; partially ordered sets; simple combinations; truth tables; Boolean algebras and elementary group theory, with applications to logic networks, trees and languages; binary coding theory and finite-state machines. Cross-listed as MATH 277. Prerequisites: MATH 101, 102 or 107 or 127 or 122 or CSCI 162. Three credits.

340 Evolutionary Computation  
Evolutionary computation is a family of powerful optimization algorithms often used to find solutions to computationally intractable problems. The study of these algorithms and their application to problems is a large research area within computer science. Course topics include combinatorial optimization, genetic algorithms, particle swarm optimization, search space analysis, multi-objective optimization, and neuroevolution. Research practices and technical writing will be emphasized for course assignments/projects. 

350 Biomedical Computation  
Technological development has transformed modern biomedical data analysis. The large amounts of biomedical data currently acquired has the potential to have real world positive impacts, however, the underlying nature of the data presents major challenges for computational biomedical analysis techniques. This course focuses on advanced technologies applied to biomedical computation, a rapidly growing field with tremendous potential for having a beneficial impact on patient care and public health. Three credits. 

444 Machine Learning 
This course covers modern technologies in computational machine learning. Validation of machine learning algorithms will be taught alongside computational design considerations for the creation of reliable and robust machine learning models. Machine learning techniques will be taught in detail from a computational technology perspective, including decision trees, bootstrapping, bagging, super learners, AdaBoost, artificial & convolutional neural networks and methods for minimizing error on unseen data. Classical learning techniques will also be presented. Prerequisites: CSCI 161, STAT 224 or 231 or 101 or permission of department chair. Three credits. 

485 Software Design  
The course covers techniques for the design and management of large software projects, including structured programming, debugging, and testing methodologies. Examples of large systems will be provided and a programming project will be completed. Prerequisite: CSCI 162; 483 is recommended. Three credits.

495 Artificial Intelligence  
An introduction to the core concepts of artificial intelligence, including state space, heuristic search techniques, knowledge representation, logical inference, uncertain reasoning, and machine learning. Specific methods covered include neural networks, genetic algorithms, and reinforcement learning. Prerequisites: CSCI 255, 263, 277. Three credits.  

In addition to the required courses, student will have the option to pick two of the courses listed below:

345 Computer Graphics  
Covers fundamental mathematical, algorithmic, and representational issues in computer graphics. Topics include graphics programming, geometrical objects and transformations, 2-D and 3-D data description, manipulation, viewing projections, clipping, shading and animation. Prerequisites: MATH 253; CSCI 255. Three credits and a two-hour lab. 

361 Natural Language Processing
This course presents students with methods to automatically analyze text written in a natural language. It explores traditional statistical methods for natural language processing before focusing on more modern techniques such as embedding-based models. This course represents approaches and their applicability across different tasks, such as sentiment analysis, machine translation, and document classification. Students are expected to code solutions for assignments and a final project. Prerequisite: CSCI 255; 223 recommended. Three credits. 

364 Mobile Application Development  
A mobile application (mobile app) is a software application designed to run on smartphones, tablet and other mobile devices. The android mobile platform has become one of the most popular mobile platforms used by millions around the world. This course introduces application development for the Android OS that can run on mobile devices. The course covers the Android system, the Android development tools, Activity Lifecycle, User Interfaces in Android, and Android application development that uses SMS, databases, location tracking, and/or multimedia. Credit will be granted for only one of CSCI 364 or CSCI 471. Prerequisite: CSCI 162 or INFO 256. 

455 Parallel and Distributed Computing  
Introduces parallel programming techniques as a natural extension to sequential programming. Students will learn techniques of message-passing parallel programming; study problem-specific algorithms in both non-numeric and numeric domains. Topics will include: numeric algorithms; image processing and searching; optimization. Prerequisites: CSCI 263; 375 recommended. Three credits and a two-hour lab. 

527 Big Data 
The emphasis of this course is to introduce big data technology. Course topics include reliable and big data storage, efficient big data processing and analytics, and important Spark APIs. Students will gain abilities to design highly scalable systems that can store, process, and analyze a big volume of unstructured and/or semi-structured data in batch mode and/or real time. Three credits.   

Students in the Post-Baccalaureate Diploma will take an intensive series of existing undergraduate courses. The program consists of 48 credits in Computer Science, with most credits clustered around the field of Artificial Intelligence. The program offers students options to explore CS topics of their choice. 

The recommended course pattern is below. The pattern may vary, but the degree requires successful completion of each of the requirements listed.

Please visit our Admissions Office site or contact the office via email at: @email.