Computer Science: Artificial Intelligence and Machine Learning Specialization, BS

First Semester

• CSC 110 Introduction to EECS Credits: 1
• CSC 115 Software Development I Credits: 3 ²
• CENG 142 Introduction to Digital Systems Credits: 2
• CENG 142L Introduction to Digital Systems Lab Credits: 1
• ENGL 101 Composition I Credits: 3 ¹
• MATH 123 Calculus I Credits: 4 ¹

Second Semester

• CSC 215 Software Development II Credits: 3 ²
• CSC 216 Introduction to C++ Credits: 3
• CSC 251 Finite Structures Credits: 3 ²
• MATH 125 Calculus II Credits: 4
• General Education Goal 3 or 4 Elective(s) Credits: 3 ¹

First Semester

• CSC 315 Data Structures & Algorithms Credits: 3 ²
• CSC 341 Software Design Credits: 3
• MATH 381 Introduction to Probability and Statistics Credits: 3
• General Education Goal 3 or 4 Elective(s) Credits: 3 ¹
• General Education Science Elective Credits: 3 ¹
• General Education Science Lab Elective Credits: 1 ¹

Second Semester

• CSC 484 Database Management Systems Credits: 3
• CENG 320 RISC Assembly Credits: 3
• CENG 320L RISC Assembly Lab Credits: 1
• MATH 225 Calculus III Credits: 4
• General Education Science Elective Credits: 3 ¹
• General Education Science Lab Elective Credits: 1 ¹

First Semester

• CSC 340 Software Engineering Credits: 3
• CSC 461 Programming Languages Credits: 3
• CENG 325 CPU & GPU Organization and Architecture Credits: 3 ²
• MATH 315 Linear Algebra Credits: 3
• ENGL 279 Communication in the STEM Workplace Credits: 3 ¹

Second Semester

• CSC 458 Operating Systems and Virtual Machines Credits: 4
• MATH 382 Probability Theory and Statistics II Credits: 3
• ENGL 289 STEM Communication for Technical and Public Audiences Credits: 3
• Specialization Elective Credits: 3 ³  
• General Education Goal 3 or 4 Elective(s) Credits: 3 ¹

First Semester

• CSC 372 Analysis of Algorithms Credits: 3
• CSC 441 Networking and Data Communications Credits: 3
• CSC 448 Machine Learning Credits: 3
• CSC 463 Capstone Design I Credits: 2
• Specialization Elective Credits: 3 ³ 

Second Semester

• CSC 454 Data Mining Theory Credits: 3
• CSC 467 Capstone Design II Credits: 2
• CSC 468 Cloud Applications Credits: 3
• Specialization Elective Credits: 3 ³ 
• General Education Goal 3 or 4 Elective(s) Credits: 3 ¹ 

Notes:

For the bachelor of science in Computer Science, a student must have a departmental grade point average of at least 2.00 in all CSC courses 300 level or higher.

Course requirements for the program also include the total number of credits per course. To meet the 88 required credits of the major, any student that has an approved course for transfer will fulfill the course requirement, but if the transferred course does not meet the total credit hour requirement then the program committee will decide on the proper accommodation for meeting the total credit hour requirement. Students should consult with their advisor to get clarification on the necessary accommodation. 

¹ Fulfills General Education requirement. Students should consult the “General Education Requirements ” section of this catalog for a complete listing of all general education requirements.

² Must have a “C” or higher to move on to the proceeding course.

³ Select nine credits from: CSC 406 , CSC 414 , CSC 424 , CSC 447 , or MATH 443 . These electives are offered on a rotational basis. Students should work closely with their advisors to make a plan to accommodate the schedule of course offerings.

Objectives and Outcomes

Program Educational Objectives:

1. Graduates will have demonstrated a mastery of their field and have the requisite foundation for lifelong learning and possess the skills to adapt and to thrive within the profession.
2. Graduates will have demonstrated their ability to assume leadership roles through career advancement or by assuming responsibilities beyond those expected of entry-level positions.
3. Graduates will be involved in their profession and make contributions to their field.

Student Outcomes:

1. Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
3. Communicate effectively in a variety of professional contexts.
4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
5. Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline
6. Apply computer science theory and software development fundamentals to produce computing-based solutions.