The Master of Science in Artificial Intelligence Engineering is an innovative graduate program that prepares you for impactful careers in the rapidly evolving field of artificial intelligence. You gain a strong foundation in both the theoretical principles and practical applications of AI, ensuring you're ready to contribute meaningfully as a skilled professional in this transformative domain.

The program welcomes students from all undergraduate disciplines in engineering and related fields—whether you have a background in AI or are exploring it for the first time. If you haven't completed prior coursework in artificial intelligence, you can take preparatory courses after admission to build the necessary foundational skills. The curriculum emphasizes experiential learning through hands-on projects and real-world applications, preparing you for success in both industry and academic research.

Requirements for the MS in Artificial Intelligence Engineering

Completion of all requirements as established by the department graduate committee, the graduate advisory committee, and Graduate Studies, to include

1. Completion of an approved program consisting of 30 units of 400/500/600-level courses as follows:
   1. Completion of at least 18 units of approved 600-level Electrical and Computer Engineering (EECE) courses.
   2. Completion of the remaining units with 400, 500, or 600-level courses in electrical and computer engineering or related disciplines, subject to approval by the Graduate Coordinator.
   3. Completion of 8 units of required core courses in artificial intelligence engineering, selected from approved 600-level Electrical and Computer Engineering (EECE) course offerings.
   4. Completion of the remaining 22 units through a combination of elective courses and an approved culminating experience, as outlined below.
2. Completion and final approval of one of the following three culminating activities as specified by the graduate advisory committee:
   1. Culminating Activity Plan: Requires the completion of 27 units of coursework and 3 units of the culminating activity course, EECE 693, which is to be taken as part of the last 9 units or during the final semester with Graduate Coordinator approval. This course may be attempted a maximum of three times.
   2. Project Plan: Consists of 24 units of coursework and 6 units of project preparation, EECE 699P. Eligibility for this plan includes a post-baccalaureate GPA of 3.5 or higher, faculty nomination, and a project proposal approved by the Graduate Coordinator. A formal written description of the project must be submitted for Graduate Studies approval and library accession.
   3. Thesis Plan: Requires the completion of 24 units of coursework and 6 units of thesis research, EECE 699T. Qualification for this plan also requires a post-baccalaureate GPA of 3.5 or above, faculty nomination for research, and a thesis proposal approved by the Graduate Coordinator. A formal research thesis is required, which must be submitted to Graduate Studies for approval and library accession.
3. Students following the Culminating Activity Plan with EECE 693 complete 19 units from the listed elective courses. Students following the Project Plan with EECE 699P or the Thesis Plan with EECE 699T complete 16 units.
4. Approval by the Graduate Coordinator and the Graduate Council on behalf of the faculty of the University.

Course List

Course	Title	Units
EECE 664	Machine Learning for Engineers	4
EECE 665	Deep Learning Processing	4
Select 16-19 units from the following:		16-19
CSCI/MATH 417	Applied Linear Algebra for Computer Science
EECE 465	Digital Signal Processing
EECE 537	Smart Device Security
EECE 565	Bioimaging Systems
EECE 566	Applied Digital Image Processing
EECE 637	Advanced Embedded Systems
EECE 644	Advanced Cryptographic Protocols for Secure Distributed Computing
EECE 682	Digital Control System Design Using Artificial Intelligence
EECE 685	Adaptive Control Systems by Artificial Intelligence
EECE 697	Independent Study
EECE 698	Seminar in Advanced Topics
Select one of the following:		3-6
EECE 693	Research Methods in Electrical and Computer Engineering
EECE 699P	Master's Project
EECE 699T	Master's Thesis
Total Units		30

Graduate Grading Requirements

All courses in the major (with the exceptions of Comprehensive Examination - 696, Independent Study - 697, Master's Project - 699P, and Master's Thesis - 699T) must be taken for a letter grade, except those courses specified by the department as ABC/No Credit (400/500-level courses), AB/No Credit (600-level courses), or Credit/No Credit grading only. A maximum of 10 units combined of ABC/No Credit, AB/No Credit, and Credit/No Credit grades may be used on the approved program (including 696, 697, 699P, 699T and courses outside the major). While grading standards are determined by individual programs and instructors, it is also the policy of the University that unsatisfactory grades may be given when work fails to reflect achievement of the high standards, including high writing standards, expected of students pursuing graduate study.

Students must maintain a minimum 3.0 grade point average in each of the following three categories: all coursework taken at any accredited institution subsequent to admission to the master's program; all coursework taken at Chico State subsequent to admission to the program; and all courses in the approved master's degree program. Failure to maintain a 3.0 average in any category will result in academic notice in the master's program. Failure to remedy the deficiency within one semester with appropriate courses approved by the program coordinator may result in disqualification from the master's program. See Graduate Education Policies for more information.

In addition, students may not count more than two courses in which they receive a grade of C toward the approved program.

Continuous enrollment is required. At the discretion of the academic program, a maximum of 30 percent of the units counted toward the degree requirements may be special session credit earned in non-matriculated status combined with all transfer coursework. This applies to special session credit earned through Open University, or in courses offered for academic credit through Professional & Continuing Education. Correspondence courses and UC Extension coursework are not acceptable for transfer.

Graduate Time Limit

All requirements for the degree are to be completed within five years of the end of the semester of enrollment in the oldest course applied toward the degree. See Master's Degree Requirements in the University Catalog for complete details on general degree requirements.

Graduate Requirement in Writing Proficiency

All students must demonstrate competency in writing skills as a graduation requirement by successfully completing EECE 693, EECE 699P, or EECE 699T.

Prerequisites for Admission to Conditionally Classified Status

1. Meet all Graduate Studies requirements as specified in Graduate and Postbaccalaureate Admission Requirements
2. Approval by the department and Graduate Studies.
3. An acceptable baccalaureate in engineering, math, physics, computer science, or a closely related field from an accredited institution, or an equivalent approved by Graduate Studies.
4. If the undergraduate program is not ABET-accredited, GRE is required with a minimum GRE q+v of 294 and GRE AWA of 3.0. A higher GRE q+v minimum may be required depending on the number of applicants.
5. Submission of a statement of purpose and two letters of recommendation.
6. International applicants must meet Chico State’s minimum requirements on English language proficiency tests.

Prerequisites for Admission to Classified Status

In addition to the requirements listed above

1. Completion of college-level coursework in programming and algorithms.
2. Completion of a two-semester sequence of college-level calculus.
3. Completion of college-level coursework in probability and statistics.
4. Students whose academic background is deemed insufficient may be required to complete preparatory coursework. These preparatory courses taken to achieve classified graduate status will not count toward the MS degree requirements.
5. Meet Graduate Studies requirements.

Advancement to Candidacy

In addition to the requirements listed above

1. Classified graduate standing and completion at the University of at least 15 units of the proposed program with a minimum 3.00 grade point average.
2. Development of an approved program in consultation with the Graduate Coordinator.
3. Formation of the graduate advisory committee, in the case of the thesis or project plan in consultation with the Graduate Coordinator.
4. Meet Graduate Studies requirements.

Program Learning Outcomes (PLOs) are clear, measurable statements that describe how students demonstrate mastery of the knowledge, skills, and values of the discipline as appropriate to the degree. Graduates of this program are expected to achieve the following PLOs:

1. Advance and apply their knowledge and analytical skills in artificial intelligence and related computational techniques to the analysis and design of advanced intelligent systems. This outcome emphasizes the ability to deepen understanding and utilize AI principles for sophisticated system development.
2. Acquire the preparation that enables them to maintain currency and conduct research in the evolving fields of artificial intelligence, machine learning, and related domains. This highlights the program's commitment to fostering lifelong learning and research capabilities in AI.
3. Ensure they can communicate their technical ideas and findings effectively through clear written, oral, and visual presentations. This underscores the importance of professional communication in the multidisciplinary AI field.
4. Elevate the technical expertise of practicing engineers and computer scientists within the specialized domain of artificial intelligence. This signifies the program's relevance for professionals seeking to enhance their AI capabilities.
5. Be prepared to excel in doctoral programs within artificial intelligence, electronics, computer engineering and science, or closely related disciplines. This indicates the program's rigorous academic standards and its potential to serve as a strong foundation for further academic pursuits.
6. Demonstrate the ability to identify and formulate precise requirements for complex intelligent systems. This focuses on the critical skill of defining the specific needs for advanced AI systems.
7. Exhibit the capacity to analyze and prioritize diverse requirements and constraints to determine the essential features of advanced intelligent systems. This emphasizes the ability to make informed decisions in the design process of AI systems.
8. Possess the skill to design and implement intelligent systems effectively by utilizing appropriate AI and machine learning techniques to meet clearly defined specifications. This outcome focuses on the practical application of AI principles in the creation of functional systems.
9. Develop the ability to conduct thorough literature research and critically evaluate its impact on addressing challenges in artificial intelligence and related fields. This highlights the importance of staying informed and rigorously assessing existing research in AI.
10. Demonstrate the aptitude to apply current AI technologies and utilize modern computational tools effectively to solve complex engineering and computational problems. This emphasizes the practical application of contemporary AI tools and techniques in relevant contexts.
11. Recognize and uphold professional responsibilities and make well-informed judgments in AI practice based on established legal and ethical principles. This highlights the crucial ethical considerations within the development and deployment of AI.
12. Function effectively as collaborative members or leaders within teams engaged in activities relevant to the field of artificial intelligence. This emphasizes the inherently collaborative nature of AI development and research.