Artificial Intelligence - Misericordia University

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Bachelor of Science in Artificial Intelligence

Learn to design ethical, robust AI systems by combining mathematics, computer science, and hands-on machine learning. Build production-ready models, evaluate real-world impact, and graduate career-ready.

AI Stats

According to LinkedIn’s Jobs on the Rise 2025, “Artificial Intelligence Engineer” is the #1 fastest‑growing job title in the U.S. (data from millions of job‑moves, Jan 2022–Jul 2024).

Projected Growth

17–20% AI / related tech roles

vs ~4% for all occupations

#1 Fastest‑Growing Job

AI Engineer LinkedIn Jobs on the Rise 2025

Data window: Jan 2022–Jul 2024

Typical Starting Salary

$157k–$179k Machine Learning Engineers

Based on Indeed & Glassdoor, Oct 2025

Sources: BLS Employment Projections (2024–34); Indeed salary data (Oct 2025); Glassdoor salary data (Oct 2025). LinkedIn Business (Jan 2025).

Why Misericordia AI

Four reasons our grads stand out from day one.

#1

Hands-On
Build & Evaluate AI

Projects in TensorFlow & PyTorch—design, train, deploy.
#2

Interdisciplinary
CS + Math/Stats + Data

From programming foundations to advanced ML—built for real-world impact.
#3

Responsible
Ethical & Human-Centered

Bias, privacy, accessibility—baked into coursework and projects.
#4

Career-Ready
High-Growth Roles

AI/ML Engineer, Data Scientist, Research Dev—portfolio to placement.

Important Information

Careers in AI

Careers

Graduates are prepared for roles such as AI Engineer, Machine Learning Engineer, Data Scientist, and Applied Researcher. The program emphasizes practical, job-ready skills: Python, TensorFlow/PyTorch, data pipelines, model evaluation, and ethics.

AI Engineer

Build and deploy ML models in production; collaborate with engineers to integrate AI into products.

Machine Learning Engineer

Design algorithms, feature pipelines, and optimize models for scale and inference speed.

Data Scientist

Analyze data, create models, and communicate insights to stakeholders using reproducible workflows.

Research Software Engineer

Bridge research and engineering—prototype new methods, optimize code, and enable reproducible experiments.

Program Learning Outcomes

Graduates will leave with deep technical knowledge and the ethical, critical thinking skills to design responsible AI systems.

Demonstrate proficiency in foundational and advanced AI

Gain competency in search, planning, probabilistic models, and core machine learning techniques.

Evaluate AI systems

Assess model performance, fairness, robustness, and deployment trade-offs with standard metrics and tests.

Demonstrate critical thinking skills

Analyze problems, select suitable methods, and interpret results responsibly in real contexts.

Apply mathematical & statistical foundations

Use linear algebra, probability, and optimization to build, tune, and analyze AI algorithms.

Analyze societal, ethical, and professional implications

Detect bias, address privacy, and integrate human-centered design and professional responsibility.

Career Supports

We don’t just teach AI—we launch AI careers.

Personalized Career Coaching

One-on-one resume reviews, mock technical interviews, and recruiting prep tailored to AI, ML, and data roles.
Internships & Co-ops

Guided search strategy across PA/NY/NJ with faculty advising on role fit and project scope; credit-bearing options align with upper-level AI/ML courses.
AI Practicum & Capstone

Production-quality projects emphasizing model design, evaluation (accuracy/precision/recall), and deployment—mapped to program learning outcomes.
Tools & Certifications

Hands-on with TensorFlow & PyTorch in coursework and labs; structured prep for cloud ML and data engineering micro-credentials.
Ethical & Responsible AI

Curriculum outcomes on societal impact, fairness, privacy, and HCI—informing portfolio work and interview narratives.
Research & High-Performance Computing

Collaborate with faculty on applied AI and leverage HPC coursework to accelerate training/inference for R&D roles.

Outcomes & Roles

Prepared for roles such as ML Engineer, AI Engineer, Data Scientist, Applied Researcher, and MLOps/Model Evaluation.

Career Readiness Timeline

Year 1–2: Core programming, data structures, discrete math; résumé foundations.
Year 2–3: Intro to AI & ML, statistics sequence; internship search + mock interviews.
Year 3–4: Advanced AI/ML, databases, software design, HPC; capstone + showcase + placement support.

Curriculum

Curriculum & sample 4‑year roadmap

Core CPS and MTH courses, AI core sequence, AI electives, general education — total 120 credits. Below is a condensed roadmap.

Year 1

Intro to Programming (Python) — Discrete Structures — Calculus I — General Education

Year 2

Data Structures & Algorithms — Linear Algebra — Probability & Statistics — AI Foundations

Year 3

Machine Learning I — Systems & Databases — Ethics in AI — Electives / Research Project

Year 4

Machine Learning II — Advanced Topics (NLP, Vision, Reinforcement Learning) — Capstone Project — Internship

Download full curriculum (pdf)

FAQs

Minor in AI

The Artificial Intelligence minor offers undergraduates an exciting opportunity to explore the foundations and applications of AI, blending theory with practical skills. Designed for students across disciplines, the program introduces key topics such as machine learning, neural networks, natural language processing, and ethical AI development. Through hands-on projects and interdisciplinary coursework, students gain proficiency in programming, data analysis, and AI system design, preparing them to innovate in fields like healthcare, finance, and technology.

Required Minor Courses:
MTH 115 or BUS 299
CPS 101
CPS 121
CPS 232
AI 3XX
AI 3YY

Mission Statement

In alignment with the University Mission Statement that tasks academic programs to develop students’ cognitive as well as community facing problem solving skills, the Misericordia University Artificial Intelligence program prepares our students to analyze, design, and implement AI systems, fostering a generation of ethical, innovative graduates who can shape the future of technology by giving them a rigorous academic foundation in computer science, mathematics, and machine learning while also instilling a strong sense of responsibility toward the moral and societal implications of AI. Our students will understand the importance of designing transparent, fair, and inclusive AI systems that respect human dignity, promote equity, and contribute positively to society. By blending technical excellence with ethical considerations, our students will advance AI technologies with a focus on benefitting humanity and addressing global challenges.