ENGL 1101  English Composition I

3 Credits

A composition course focusing on skills required for effective writing in a variety of contexts, with emphasis on exposition, analysis, and argumentation, and also including introductory use of a variety of research skills.

POLS 1101  American Government

3 Credits

This course examines the institutions and processes of American government and Georgia State government. Global comparisons are made between the governments of the U.S. and other modern nation-states.

ECON 1000  

Credits

Description not available

CSE 1321  

Credits

Description not available

CSE 1321L  

Credits

Description not available

General Education Core Curriculum- Mathematics and Quantitative Skills

3 Credits

MATH 1113 or MATH 1190

ENGL 1102  English Composition II

3 Credits

A composition course that develops writing skills beyond the levels of proficiency required by ENGL 1101, that emphasizes interpretation, and evaluation, and that incorporates a variety of more advanced research methods.

CSE 1322  

Credits

Description not available

CSE 1322L  

Credits

Description not available

MATH 2345 or CSE 2300

3 Credits

MATH 2345 or CSE 2300

General Education Core Curriculum- Technology, Mathematics and Sciences Applied Math

4 Credits

MATH 1179 or MATH 1190 or MATH 2202

General Education Core Curriculum- Political Science and US History

3 Credits

HIST 2111 or HIST 2112

MATH 3260  Linear Algebra I

3 Credits

An introduction to linear algebra and some of its classical and modern applications. Among topics to be included will be systems of linear equations, matrices, determinants of matrices and applications, vector spaces, and inner product spaces. Significant use of technology will be employed in performing matrix computations.

CS 3305  Data Structures

3 Credits

This course introduces data structures, specification, application, and implementation. The case studies will illustrate how data structures are used in computing applications. The emphasis of the course is on linear and some nonlinear data structures and object oriented principles. Topics include: abstract data types, stacks, queues, lists, binary search trees, priority queues, recursion, algorithm efficiency, trees, heaps, hash tables, and analysis of search and sort algorithms and their performance for implementation and manipulation. The programming language to be used in this course is any standard high-level object-oriented programming language such as C++, Java, and Ada.

AI 2000  Intro to AI Programming

3 Credits

This course provides an overview of the artificial intelligence (AI) discipline and introduces the student to the fundamental principles and processes of artificial intelligence programming. This course highlights the overview of AI programming and the activities performed at each stage in the development cycle such as the introduction to AI platform (GPU) and software tools (Python, Colab), Python AI Framework (TensorFlow, PyTorch), AI data types and structures (list, matrice), AI dataset, AI ethical principles and a brief introduction to machine learning and deep learning. Through application case studies students design, implement, and test AI solutions to solve problems.

General Education Core Curriculum- Arts and Humanities

3 Credits

WLC 1001 or WLC 1002 or PHIL 2010 or WLC 2209 or ENGL 2110 or ENGL 2120 or ENGL 2130 or ENGL 2140

General Education Core Curriculum- Social Sciences

3 Credits

ANTH 1102 or CRJU 1101 or ECON 2105 or ECON 2106 or GEOG 1101 or PSYC 1101 or SOCI 1101 or STS 1101

STAT 2332  Probability and Data Analysis

3 Credits

This course is a foundational, calculus-based introduction to statistics and probability. The following conceptual themes will be developed through the process of statistical investigation: exploratory data analysis (univariate and bivariate), fundamentals of experiment design and sampling, planning and conducting a study, exploring random phenomenon using probability and simulation, and the fundamentals of statistical inference. Technology is integrated into each theme, and the statistical software package used will be chosen by the instructor.This course is a foundational, calculus-based introduction to statistics and probability. The following conceptual themes will be developed through the process of statistical investigation: exploratory data analysis (univariate and bivariate), fundamentals of experiment design and sampling, planning and conducting a study, exploring random phenomenon using probability and simulation, and the fundamentals of statistical inference. Technology is integrated into each theme, and the statistical software package used will be chosen by the instructor.

CS 3410  Intro to Database Systems

3 Credits

Introduction to the database management systems, database processing, data modeling, database design, development, and implementation. Contrasts alternative modeling approaches. Includes implementation of current DBMS tools and SQL.

AI 3642  Artificial Intelligence

3 Credits

The primary objective of this course is to provide an introduction to the basic principles and applications of Artificial Intelligence. It covers the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, perception and action, and learning ? and their applications. Students will design and implement key components of intelligent agents of modern complexity and evaluate their performance. Students are expected to develop familiarity with current research problems, research methods, and the research literature in AI.

General Education Core Curriculum- Technology, Mathematics and Sciences

3 Credits

ANTH 1105 or GEOG 1110 or GEOG 1112K or GEOG 1125 or SCI 1101K or SCI 1102 or (BIOL 1107 and BIOL 1107L) or (BIOL 1108 and BIOL 1108L) or (CHEM 1151 and CHEM 1151L) or (CHEM 1152 and CHEM 1152L) or (CHEM 1211 and CHEM 1211L) or (CHEM 1212 and CHEM 1212L) or (PHYS 1111 and PHYS 1111L) or (PHYS 1112 and PHYS 1112L) or (PHYS 2211 and PHYS 2211L) or (PHYS 2212 and PHYS 2212L)

Free Elective (1 of 4)

3 Credits

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CS 3503  Computer Organization & Arch

3 Credits

Introduction and overview of basic computer organization. Computer arithmetic: binary, hexadecimal and decimal number conversions, binary number arithmetic and IEEE binary floating point number standard. Basic computer logic: gates, combinational circuits, sequential circuits, adders, ALU, SRAM and DRAM. Basic assembly language programming, basic Instruction Set Architecture (ISA), and the design of single cycle CPU. Hardware security will be introduced.

CSE 3801  Professional Practices & Ethic

2 Credits

This course covers the historical, social and economic consideration of the discipline. It includes studies of professional conduct, risks, and liabilities, and intellectual property relative to the software engineering and computing professions. Software engineering/computing case studies will be used.

AI 4267  Machine Learning

3 Credits

This course provides a broad introduction to machine learning and statistical pattern recognition including supervised, unsupervised, and ensemble learning. Topics include K-NN, Naive Bayes Classifier, parametric and non-parametric methods, support vector machines, kernel machines, neural networks, clustering, dimensionality reduction, and model evaluation. The learning theory including bias/variance tradeoffs and large margins will be introduced. The course will also discuss recent applications of machine learning such as data mining, autonomous navigation, speech recognition, and text and web data processing.

General Education Core Curriculum- Technology, Mathematics and Sciences

4 Credits

GEOG 1112K or GEOG 1113K or SCI 1101K or (BIOL 1107 and BIOL 1107) or (BIOL 1108 and BIOL 1108) or (CHEM 1151 and CHEM 1151L) or (CHEM 1152 and CHEM 1152L) or (CHEM 1211 and CHEM 1211L) or (CHEM 1212 and CHEM 1212L) or (PHYS 1111 and PHYS 1111L) or (PHYS 2211 and PHYS 2211L) or (PHYS 2212 and PHYS 2212L) or (PHYS 1112 and PHYS 1112L)

Major Elective (1 of 5) Students should complete a series of major elective courses or a minor. It is recommended for students to consult with an academic advisor to make their selections. Major Electives: Complete 15 credit hours of AI, CS or CSE 3000-4000 level coursework. OR Minor: Students are encouraged to complete a formal minor that focuses on a particular domain with AI/ML applications, e.g., art, business, computing, engineering, healthcare, management, or science fields. When pursuing minors that require more than 15 credit hours, students may use the University Electives to complete the corresponding requirements.

3 Credits

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AI 4277  Deep Learning

3 Credits

The course covers the foundations of Deep Learning; how to build neural networks and how to design successful deep learning projects. The course topics include convolutional networks, sequence modeling such as recurrent and recursive neural networks (RNNs), long short-term memory (LSTM), Adam, Dropout, BatchNorm, Xavier/He initialization, state-of-the-art technologies, and research topics leveraging Deep Learning. The course includes programming assignments in Python and in TensorFlow.

AI 4732  Machine Vision

3 Credits

This course introduces concepts and techniques in machine vision. Students successfully completing this course will be able to apply a variety of image processing techniques for the design and analysis of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. Topics include basic image enhancement, corner and edge detection, image morphology, linear and non-linear filters, image transformations, camera models, two-dimensional and three-dimensional image geometry, clustering and segmentation (and classification), object recognition and Bag-of-Words models, image texture, shape analysis, and tracking.

General Education Core Curriculum- Social Sciences

3 Credits

HIST 1100 or HIST 1111 or HIST 1112

Major Elective (2 of 5) Students should complete a series of major elective courses or a minor. It is recommended for students to consult with an academic advisor to make their selections. Major Electives: Complete 15 credit hours of AI, CS or CSE 3000-4000 level coursework. OR Minor: Students are encouraged to complete a formal minor that focuses on a particular domain with AI/ML applications, e.g., art, business, computing, engineering, healthcare, management, or science fields. When pursuing minors that require more than 15 credit hours, students may use the University Electives to complete the corresponding requirements.

3 Credits

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Free Elective (2 of 3)

4 Credits

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CS 4306  Algorithm Analysis

3 Credits

Advanced algorithm analysis including the introduction of formal techniques and the underlying mathematical theory. Topics include asymptotic analyses of complexity bounds using big-O, little-o, omega, and theta notations. Fundamental algorithmic strategies (brute-force, greedy, divide-and-conquer, backtracking, branch-and-bound, pattern matching, parallel algorithms, and numerical approximations) are covered. Also included are standard graph and tree algorithms. Additional topics include standard complexity classes, time and space tradeoffs in algorithms, using recurrence relations to analyze recursive algorithms, NP-completeness, the halting problem, and the implications of non-computability.

AI 4742  Natural Language Processing

3 Credits

The course provides an introduction to the field of natural language processing (NLP). The topics include creating systems that can understand and produce language, for applications such as information extraction, machine translation, automatic summarization, question-answering, and interactive dialogue systems. The course covers linguistic (knowledge-based) and statistical approaches to language processing in the areas of syntax (language structures), semantics (language meaning), and pragmatics/discourse (the interpretation of language in context). Students will design and develop programs for analyzing and extracting information from large online corpora.

General Education Core Curriculum- Institutional Priorities

3 Credits

AMST 1102 or ASIA 1102 or BLCK 1102 or COMM 1100 or GWST 1102 or ISD 2700 or LALS 1102 or LDRS 2300 or PAX 1102 or POLS 2401 or RELS 1102

Major Electives (3 of 5) Students should complete a series of major elective courses or a minor. It is recommended for students to consult with an academic advisor to make their selections. Major Electives: Complete 15 credit hours of AI, CS or CSE 3000-4000 level coursework. OR Minor: Students are encouraged to complete a formal minor that focuses on a particular domain with AI/ML applications, e.g., art, business, computing, engineering, healthcare, management, or science fields. When pursuing minors that require more than 15 credit hours, students may use the University Electives to complete the corresponding requirements.

3 Credits

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Free Elective (3 of 3)

3 Credits

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AI 4850  AI Capstone

3 Credits

The course provides a capstone experience for AI majors to promote a successful transition to the workplace or further academic study. Students will have the opportunity to practice essential project management skills and work with current AI tools and technologies. Each team will designate a team leader who is responsible for coordinating work tasks, team meetings, communications with the instructor and/or project sponsor, and team effort.

General Education Core Curriculum- Arts and Humanities

3 Credits

ART 1107 or DANC 1107 or MUSI 1107 or TPS 1107

Major Electives (4 of 5) Students should complete a series of major elective courses or a minor. It is recommended for students to consult with an academic advisor to make their selections. Major Electives: Complete 15 credit hours of AI, CS or CSE 3000-4000 level coursework. OR Minor: Students are encouraged to complete a formal minor that focuses on a particular domain with AI/ML applications, e.g., art, business, computing, engineering, healthcare, management, or science fields. When pursuing minors that require more than 15 credit hours, students may use the University Electives to complete the corresponding requirements.

3 Credits

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Major Elective (5 of 5) Students should complete a series of major elective courses or a minor. It is recommended for students to consult with an academic advisor to make their selections. Major Electives: Complete 15 credit hours of AI, CS or CSE 3000-4000 level coursework. OR Minor: Students are encouraged to complete a formal minor that focuses on a particular domain with AI/ML applications, e.g., art, business, computing, engineering, healthcare, management, or science fields. When pursuing minors that require more than 15 credit hours, students may use the University Electives to complete the corresponding requirements.

3 Credits

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