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Artificial Intelligence and Machine Learning
Jesse Wallace, MS, Chair
Phone: (419) 995-8356
Email: wallace.j@rhodesstate.edu
Office: JJC 131
The Associate in Science (AS) in Artificial Intelligence and Machine Learning Major focuses on building machine learning models that can be used for predicting, making decision,s and enhancing human capabilities. The program prepares students for entry-level positions in a variety of fields using artificial intelligence, including the information technology, automotive, healthcare, aerospace, industrial, and manufacturing industries. Program content includes an introduction to artificial intelligence and machine learning, natural language processing, computer vision, and artificial intelligence for business solutions and other applications. The curriculum also includes coursework in computer programming, math, engineering and statistics.
Program Learning Outcomes
Upon completion, the student will be able to:
- Apply common artificial intelligence (AI) concepts and methodologies, including neural networks/deep learning, machine learning, natural language processing, computer vision, and data science, for analysis and decision making.
- Apply artificial intelligence (AI) project development and machine learning life cycle to address social and business issues, opportunities, and problems.
- Apply statistical analysis and machine learning algorithms to predict usefulness of artificial intelligence (AI) programming solutions.
- Use appropriate programming languages to implement artificial intelligence (AI) solutions.
- Communicate in varied settings, orally and visually and in writing, in a culturally responsive manner.
- Collaborate with diverse individuals and teams to design and implement artificial intelligence (AI) and machine learning solutions.
- Evaluate issues of bias, culture, environment, ethics, regulations, and professional expectations in the field of artificial intelligence (AI) and machine learning.
Technical Standards
See here for details.
| First Year | ||
|---|---|---|
| Pre-requisite Semester | Hours | |
| COM 1110 | English Composition | 3 |
| CPT 1050 | Technology Basics for IT Pro | 3 |
| MTH 1260 | Statistics | 3 |
| PSY 1010 | General Psychology | 3 |
| SDE 1010 | First Year Experience | 1 |
| Term Hours | 13 | |
| Fall | ||
| AIM 1000 | Introduction to Artificial Intelligence | 3 |
| HST 1610 | American History to 1877 | 3 |
| MTH 1711 | Calculus I | 5 |
| POL 1010 | Introduction to Political Science | 3 |
| Term Hours | 14 | |
| Spring | ||
| COM 1140 | Technical Writing | 3 |
| CPT 1110 | Introduction to Programming Logic and Design | 3 |
| CPT 2350 | Database Programming | 3 |
| MTH 1721 | Calculus II | 5 |
| Term Hours | 14 | |
| Second Year | ||
| Fall | ||
| AIM 1100 | Introduction to Machine Learning | 3 |
| AIM 2991 | AIM Field Experience | 1 |
| LIT 2210 or LIT 2215 | Introduction to Literature or Native American Literature | 3 |
| PHY 1120 | Physics I | 4 |
| Term Hours | 11 | |
| Spring | ||
| AIM 2200 | Natural Language Processing | 3 |
| AIM 2220 | Artificial Intelligence for Computer Vision | 3 |
| AIM 2970 | AIM Capstone | 2 |
| PHY 1130 | Physics II | 4 |
| Term Hours | 12 | |
| Total Hours | 64 | |
AIM 1000 — Introduction to Artificial Intelligence
Credit Hours: 3.00
Total Contact Hours: 40.00
Lecture Hours: 2.00
Lab Hours: 2.00
Introduces basic concepts and applications of artificial intelligence (AI), including AI project cycles. Focus on issues surrounding AI including ethics, bias, culture, regulations, and professional expectations.
AIM 1100 — Introduction to Machine Learning
Credit Hours: 3.00
Total Contact Hours: 40.00
Lecture Hours: 2.00
Lab Hours: 2.00
Introduces machine learning concepts and Python applications, including data acquisition, supervised and unsupervised learning, and data modeling.
Prerequisites: AIM 1000, CPT 2350
Corequisites: MTH 1260.
AIM 2200 — Natural Language Processing
Credit Hours: 3.00
Total Contact Hours: 40.00
Lecture Hours: 2.00
Lab Hours: 2.00
Introduces the fundamental concepts in Natural Language Processing (NLP) and text processing. Focus on knowledge and skills necessary to create a language recognition application.
Prerequisites: AIM 1100
Corequisites: AIM 2220, AIM 2970.
AIM 2220 — Artificial Intelligence for Computer Vision
Credit Hours: 3.00
Total Contact Hours: 40.00
Lecture Hours: 2.00
Lab Hours: 2.00
Understands and applies the basic techniques to process images using OpenCV and Python libraries. Focuses on knowledge & skills necessary to apply AI in CV for common tasks like Image Classification and Object Detection.
Prerequisites: AIM 1100
Corequisites: AIM 2200, AIM 2970.
AIM 2970 — AIM Capstone
Credit Hours: 2.00
Total Contact Hours: 40.00
Lecture Hours: 4.00
Focuses on how a social issue is explored, brought through the Artificial Intelligence (AI) Project cycle, and delivered as a solution using the different domains of AI, including computer vision and natural language processing.
Prerequisites: AIM 1000, AIM 1100, COM 1110
Corequisites: AIM 2200, AIM 2220.
AIM 2991 — AIM Field Experience
Credit Hour: 1.00
Total Contact Hour: 10.00
Lecture Hour: 1.00
Enables work activity which relates to an individual student's occupational objectives. With permission of a faculty advisor, the field experience replaces elective or required courses in a student's associate degree program. The experience is coordinated by a faculty member of the college who assist the student in planning the experience, visits the site of the experience for a conference with the student and his/her supervisor at least once during the semester and assigns the course grade to the student after appropriate consultation with the employer/supervisor.
Prerequisites: AIM 1000 and faculty advisor approval.