Careers in Artificial Intelligence
- AI Solutions Architect
- Computer Vision Analyst
- Data Scientist
- Ethical AI Specialist
- Language Model Trainer
- Machine Learning Engineer
- Natural Language Processing (NLP) Engineer
- Prompt Engineer
- Research Scientist
Artificial Intelligence (AI) is a multidisciplinary field of computing that integrates principles from machine learning, natural language processing, computer vision, robotics, generative AI, and large language models (LLMs) to develop algorithms and software enabling machines to perform tasks that typically require human intelligence. As the field of AI continues its rapid advancement, it is pushing the boundaries of technological possibilities and is expected to be in great demand within the future workforce.
$98-103k
average salary for this degree*
74%
increased demand for careers in AI in the past 5 years*
1st
AI-focused bachelor's degree offered in Nebraska
Program Overview
The Bachelor of Science in Artificial Intelligence program provides students with the opportunity to build core skills in a range of AI topics including Data Analytics, Machine Learning, and Natural Language Processing, to name a few.
The program also features a flexible curriculum that allows students to combine their AI-expertise with interdisciplinary elective coursework from across campus, helping prepare the next generation of innovators in applied artificial intelligence.
Key Coursework
Concepts in AI (AIML 2060)
This course will introduce students to the foundational concepts in Artificial Intelligence and provide a broad overview of topics including representation, heuristic search, automated problem-solving, decision-making, and machine learning.
Data Analytics in Python (CSCI 2410)
This course is an introduction to the basic concepts and principles of data analytics using Python language. The course emphasizes applying Python, libraries and special software packages to data munging, analysis, mining, and visualization, and machine learning techniques including statistical analysis, parameter estimation, regression, classification, predictive modeling etc.
Fundamentals & Algorithms of Machine Learning (CSCI 3470)
This course discusses the fundamentals and algorithms of machine learning. Topics include supervised learning such as Decision Tree, Perceptron, Support Vector Machine, Naive Bayes, and Regression, unsupervised learning such as clustering, dimensionality reduction, kernel methods, learning theory such as bias/variance trade-offs, generalization, overfitting, and large margins.
Principles of AI (CSCI 4450)
An introduction to principles of artificial intelligence. The course will cover topics such as machine problem solving, uninformed and informed searching, propositional logic, first order logic, approximate reasoning using Bayesian networks, temporal reasoning, planning under uncertainty and machine learning.
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