MASTER OF SCIENCE IN APPLIED ARTIFICIAL INTELLIGENCE
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AT A GLANCE
Earn your master’s degree online or on-campus.
Regular intakes throughout the year.
Online
On-Campus FT
Program Length:
20 Months
Total Units:
30 Units
Approximate Total Fees in INR:
12,49,000
Next Term Starts:
Sep 01, 2026
Priority App Deadline:
June 30, 2026
Program Length:
16 Months
Total Units:
30 Units
Cost Per Unit:
$2,000
Next Term Starts:
January 2027
Priority App Deadline:
July 31
Quick Facts
Program consists of 10 courses: 2 courses per semester for online students, 3-4 per semester for on-campus.
ABOUT THE PROGRAM
The University of San Diego’s innovative AI master’s degree program (online or on-campus) is committed to training current and future artificial intelligence professionals for the important and fascinating work ahead. The strengths of our program include a significant emphasis on real-world applications, ethics, moral responsibility, and social good in designing AI-enabled systems.
Customize Your Experience: Online or Campus, Part-time or Full-time
Designed to prepare graduates for success in this vitally important and fast-growing field, this master’s in applied artificial intelligence master’s degree program has been developed by AI experts in close collaboration with key industry and government stakeholders to provide in-depth practical and technical training.
10 courses
Take 10 courses, totaling 30 units. (Full-time campus students take 3 courses in the Fall, 4 in the Spring, and another 3 the next Fall)
2 formats
Choose our 100% online format across 5 semesters, or join us for in-person classes 2-3 times per week in San Diego over 3 semesters
Courses you will take
Industry Insights
51.8%
The AI market share of the IT services industry in India
$15.7 trillion
AI’s predicted contribution to the global economy by 2030
INR 1 Million
Average annual salary of an AI Developer in India
Sources: PwC, Statista.com, Glassdoor.com
A closer look – artificial intelligence at usd
Designed by Leading Innovators and Educators
University of San Diego faculty bring deep tech expertise to the programs, guided by elite advisory boards of Silicon Valley veterans, entrepreneurs, and industry leaders. The curriculum is refreshed annually to align with evolving industry demands.
Damien Benveniste, PhD
Founder, TheAIEdge AI Program Advisory Board Member
Eric Colson
DS & ML Advisor, Activation Fund AI Program Advisory Board Member
Imane Khalil, PhD
Associate Dean of Graduate Programs and Professor, Mechanical Engineering
Albert Wang
Sr. Investment Director, Qualcomm Ventures AI Program Advisory Board Member
Vitor Carvalho, PhD
Principal Applied Science Manager at Microsoft AI Program Advisory Board Member
Chell Roberts, PhD
Dean, Shiley-Marcos School of Engineering
What can I do with this degree?
The program is an ideal launchpad for graduates to work in one of the world’s fastest-growing domains in high-impact positions such as:
- AI Engineer
- Machine Learning Engineer
- Natural Language Processing Scientist
- Robotics Engineer
- Software Engineer
- Software Developer
- Computational Linguist
- Human-Centered Machine Learning
- Designer
This program is ideally suited to those with a background in science, mathematics, engineering, healthcare, statistics, or technology. While these are not requirements, we advise potential students to review program requirements and expectations prior to applying to ensure the chosen program is best suited for your background and/or future goals.
Potential students are encouraged to review the available resources to gauge program readiness. This is a good way to gain a good sense of basic technical background and time management skills.
Program Outcomes
This program enables you to gain expertise in the application of diverse cutting-edge AI technologies across industries related to technology, operations, health care, defense, finance, and marketing. Upon graduation, you will:
- Apply diverse AI technologies that leverage data to enable automated business decision making, develop programs for information extraction and interface with databases, as well as have an in-depth knowledge of IoT that has a widespread usage across smart homes, self-driving cars and more.
- Develop AI systems within the legal framework while following ethical standards and socially responsible practices.
- Effectively propagate the value of AI-based systems and software among organizations.
WHERE USD GRADUATES WORK
Testimonials
Frequently Asked Questions
The program is called “Applied Artificial Intelligence” - how is this different from a generic AI or data science certificate course?
The 30-unit curriculum is built as a full graduate degree, not a single-skill workshop. Students move through probability and statistics, classical and deep learning, computer vision, and NLP, generative AI, and agentic AI, before concluding with Machine Learning Operations (MLOps) as a required course rather than an elective add-on.
Each course pairs the underlying statistical and engineering foundation with a hands-on Python project, so students aren’t sampling AI topics – they’re building the discipline underneath them, end to end.
What's the tech stack and hands-on experience like - do students actually build things, or is it mostly theory?
Hands-on Python work runs through every course: Bayesian networks and GLMs in the foundational statistics course, OpenCV and deep learning architectures in computer vision, and the Hugging Face and LangChain ecosystems for fine-tuning LLMs, building RAG pipelines, and orchestrating multi-agent systems.
Courses are supported by recorded lectures, computer lab videos, weekly live office hours, and a dedicated AI teaching assistant – this is an application-focused curriculum, not a slide-based one.
Is the curriculum current - covering generative AI, RAG, and agentic systems - or mostly classical machine learning?
Current. In the NLP, Generative AI & Agentic AI course, students build a functional RAG-based Q&A pipeline using the Hugging Face ecosystem, then work in teams to design a real-world, multi-agent system – covering inter-agent coordination and workflow patterns using frameworks like LangChain.
MLOps is then required as a graduation course rather than optional, so the discipline of properly designing, deploying, and maintaining ML systems is built into every student’s path, not left as a theoretical afterthought.
How big is the cohort - is this a high-volume program or something more selective?
What makes the capstone different from a one-off course project?
The capstone is structured as a team-based engagement linking students, instructors, and potential industry partners – not a solo academic exercise. Teams identify a real problem, develop a project proposal, then implement and evaluate the solution using the tools and techniques built across the entire program.
It’s closer to a consulting engagement than a single end-of-course assignment.
Will an online AI master's from a US university actually carry weight with employers in India and globally?
USD’s AAI advisory board includes practitioners from Netflix, Meta, Microsoft, and Qualcomm, alongside industry mentors with backgrounds spanning Cisco and GCC leadership roles at organizations including JPMorgan Chase, HSBC, and Citi.
Indian alumni have moved from largely execution-focused roles into positions such as Principal Data Scientist (Responsible AI, Agentic AI & GenAI Lead), Head – AI & Intelligent Automation, and Technical Architect, at organizations including Accenture and Rakuten.
The fee feels steep against my current salary - is there real, demonstrated ROI?
Interest in and usage of Machine Learning systems has increased dramatically in recent years. More and more innovative products and research rely on Machine Learning systems that leverage data to make predictions and identify trends. However – as with many cutting-edge fields – Machine Learning systems are often implemented improperly. As a result, many Machine Learning systems are unreliable, inefficient, or even useless. Machine Learning Operations (MLOps) is a methodology whose goal is to design, build, deploy, and maintain machine learning models properly. MLOps combines practices from Machine Learning, Data Engineering, and DevOps to ensure that Machine Learning models and algorithms are reliable, efficient, and – most importantly – useful. This course will introduce students to the key concepts of MLOps and a holistic method of designing suitable ML systems. Students will learn and perform the best practices for building Machine Learning systems with hands-on learning experiences and real-world applications. While students will learn about and implement some Machine Learning algorithms in this course, this course is not intended to teach them about the field of Machine Learning. Rather, students will learn how to properly design Machine Learning systems throughout the entire lifecycle.
Prerequisites: AAI 510, AAI 511, AAI 520, AAI 521, AAI 530, and AAI 531
Recent advances in big data, computational power, smart homes, and autonomous vehicles have rendered artificial intelligence (AI) as a major technological revolution in engineering and computer science. The goal of this course is to introduce students to the fundamental principles, techniques, challenges, and applications of AI, machine learning, and natural language processing. Topics covered include heuristic search and optimization techniques, genetic algorithms, machine learning, neural networks, and natural language understanding. Several applications of AI will be explored, including computer vision, pattern recognition, image processing, biomedical systems, Internet of Things, and robotics.
Machine learning (ML) is an interdisciplinary field that is focused on building models by algorithmic processing of data with minimal assumptions about the nature of the data. The models may be used to understand a process, make informed projections, or automate decisions. The field combines principles from statistics, computer science, and application domains. The application domains range across engineering, manufacturing, medicine, commerce, research, etc. This class will introduce students to the fundamental concepts and algorithms for machine learning. Students will learn fundamental concepts such as data cleaning and transformation, feature engineering, modeling training, validation and testing, overfitting, underfitting, and model evaluation. They will learn supervised learning algorithms such as regression, support vector machines, etc; and unsupervised learning algorithms such as k-means, Principal Component Analysis (PCA), and hierarchical clustering. Time series analysis will be briefly covered as well. Students will learn to appreciate and be sensitive to ethical issues affecting the use of machine learning in society. Prerequisites: AAI 500 and AAI 501
Neural networks have enjoyed several waves of popularity over the past half-century. The many applications of neural networks include apps that identify people in photos, automated vision systems for large-scale object recognition, smart home appliances that recognize continuous, natural speech, self-driving cars, and software that translates from any language to any other language. In this course, students will learn the fundamental principles and concepts of neural networks and state-of-the-art approaches to deep learning using in-demand Python packages, such as TensorFlow and PyTorch. Students will learn to design neural network architectures and training methods using hands-on assignments and will perform comprehensive final projects in this course. Prerequisites: AAI 500 and AAI 501