Contact Us: [email protected]
MS in Applied Artificial Intelligence
As an online student, most of your interactions with the university will be done through various websites. For example:
Ashley Dominguez
(619) 260-7521
Ebrahim Tarshizi, PhD, MBA
(619) 260-8802
Our number one priority is you! Our team has prepared a checklist of items that will set you up for success and clarify all action items as a new student. After you are enrolled for your first term and receive a confirmation email from a member of our team, please complete and review all of the following before classes start.
This webinar will go over a comprehensive look at your program and what to expect as an online learner including resources and tips for success. Each webinar should last around 30-40 minutes. Once you have been registered for classes and receive a confirmation email from our team, you will be emailed the link to schedule your welcome webinar to your @sandiego.edu email address.
In this call, you’ll “meet” a member of your Student Success Team for your program. This will be a chance for us to answer any additional questions you have before you start your first term. Please be sure to have any Canvas-related, program-related, or finance-related questions prepared. After attending the Welcome Webinar, you will be prompted to schedule your call using a scheduling link.
Once you have been registered in your classes, you will be able to access your New Student Orientation Course (AAI 550) on Canvaswithin 4 hours. When accessing Canvas, please make sure to use Firefox or Chrome as your browser.
The New Student Orientation course is designed to help you navigate your way around the course's layout prior to beginning your first class. You will learn where to find the syllabus, course schedule, assignments, and discussion board.
Your Orientation is mandatory, and must be completed before the first day of class— so we encourage you to get started! Please plan to spend about 16-20 hours completing the Orientation course. You can move through the Orientation at your own pace, so schedule your time accordingly.
Looking for assistance?
We recommend that students start this planning early as some funding sources can take some time to process. Tuition payments should be completed in full by the first day of the semester. Visit the "Tuition and Payment Methods" section for more information.
Please make sure to review your student handbook prior to the first day of class, and reference it as needed throughout your program. The handbook is where you can find information on academic expectations, drop and refund policy, technology requirements, curriculum, frequently asked questions, and more.
Below is a list of significant dates regarding the registration process, payment deadlines, and other important academic and program deadlines.
Fall 2025 Dates and Deadlines
Important Dates | Date |
---|---|
Registration Opens | June 30, 2025 |
Application Deadline | August 1, 2025 |
Registration Deadline | August 15, 2025 |
Orientation Course Due Date | August 29, 2025 |
Last Day to Drop with 100% Refund | September 1, 2025 |
Payment Due Date | September 2, 2025 |
Semester Begins | September 2, 2025 |
First Course Start Date | September 2, 2025 |
Last Day to Drop with 95% Refund/ Drop Deadline | September 5, 2025 |
Last Day to Withdraw from Course A | September 29, 2025 |
First Course End Date | October 20, 2025 |
Second Course Start Date | October 21, 2025 |
First Course Final Grade Submission Due Date | November 3, 2025 |
Last Day to Withdraw from Course B | November 17, 2025 |
Second Course Final Grade Submission Due Date | December 22, 2025 |
Semester Ends | December 8, 2025 |
Spring 2025 Dates and Deadlines
Important Dates | Date |
---|---|
Registration Opens | November 1, 2024 |
Application Deadline | December 2, 2024 |
Registration Deadline | December 16, 2024 |
Orientation Course Due Date | January 3, 2025 |
Last Day to Drop with 100% Refund | January 6, 2025 |
Payment Due Date | January 7, 2025 |
Semester Begins | January 7, 2025 |
First Course Start Date | January 7, 2025 |
Last Day to Drop with 95% Refund/ Drop Deadline | January 10, 2025 |
Last Day to Withdraw from Course A | February 3, 2025 |
First Course End Date | February 24, 2025 |
Second Course Start Date | February 25, 2025 |
First Course Final Grade Submission Due Date | March 10, 2025 |
Last Day to Withdraw from Course B | March 24, 2025 |
Second Course Final Grade Submission Due Date | April 28, 2025 |
Semester Ends | April 14, 2025 |
Summer 2025 Dates and Deadlines
Important Dates | Date |
---|---|
Registration Opens | March 3, 2025 |
Application Deadline | April 4, 2025 |
Registration Deadline | April 18, 2025 |
Orientation Course Due Date | May 2, 2025 |
Last Day to Drop with 100% Refund | May 5, 2025 |
Payment Due Date | May 6, 2025 |
Semester Begins | May 6, 2025 |
First Course Start Date | May 6, 2025 |
Last Day to Drop with 95% Refund/ Drop Deadline | May 9, 2025 |
Last Day to Withdraw from Course A | June 2, 2025 |
First Course End Date | June 23, 2025 |
Second Course Start Date | June 24, 2025 |
First Course Final Grade Submission Due Date | July 7, 2025 |
Last Day to Withdraw from Course B | July 21, 2025 |
Second Course Final Grade Submission Due Date | August 25, 2025 |
Semester Ends | August 11, 2025 |
You have most likely already filled out an Enrollment Agreement, which enables our team to register you for classes each term. No further action is required on your part.
If you are not able to register for both of your courses in a given term, please contact your program coordinator immediately. This often happens for students who need to take a leave of absence.
Students are required to have their textbooks on hand by the first day of class. Unless otherwise specified, students may select any vendor they prefer (such as Amazon.com, Half.com, Alibris.com, etc.) to purchase their course materials. In the event a specific vendor is required, it will be specified in the course materials list. The best way to ensure that you have the correct book is to search by the ISBN number(s) listed on the book list.
Physical copies of books are not on hand at the USD Torero Store. The USD Torero online store does offer price comparisons for different online vendors for some books.
Although all textbooks for all courses are listed, students only need to purchase the items for the classes they are taking for the semester.
If your course is indicated to have a "Digital Inclusive Access" textbook, you do have the option to use the integrated Vitalsource e-textbook without needing to purchase a textbook through an outside vendor. For more information, view the "Digital Inclusive Access" FAQs document.
By using Vitalsource e-Textbooks, students are able to use study tools in Bookshelf such as highlighting, printing limited pages/chapters, sharing notes, and using the Bookshelf CoachMe tool to test their learning while they read.
Tuition at USD is billed per semester, not per course. Payment (or enrollment in an official USD payment plan) is always due by the first day of the semester. Students may not carry balances from one semester to the next.
Accounts with outstanding balances after the official payment due date may be subject to course cancellations/removal or a student account hold during the semester; related holds can prevent upcoming registration, graduation, or obtaining transcripts.
Remember: tuition is always due by the first day of each semester.
Once you have been registered for your courses, your student account will reflect the appropriate tuition costs according to your program. Your program’s tuition is the following:
Please note that students in USD’s MS in Applied Artificial Intelligence program are required to submit a one-time non-refundable deposit in the amount of 5% of first semester tuition. The payment will automatically be applied to your first-semester tuition.
All students are charged a $20 fee each semester to cover a Slack Pro Account that allows you to have access to your MS-AAI Slack student and instructor community. This fee is non-refundable and cannot be waived for any students. Only active students will maintain access to their Slack Pro, and students will lose access to this account upon graduation.
Students who need to re-take or withdraw from a course may need to pay additional fees according to the Refund/Drop Deadline policies listed in your Student Handbook.
If you have any questions about your Student Account, please reach out to the Torero Hub via this request form. All costs and fees are subject to change and are based on the academic year of enrollment.
Students will be registered for their prescribed courses each semester. All courses must be dropped prior to the first day of the semester to receive a 100% tuition refund and within the first three days of the start date of the semester to receive a 95% tuition refund. No refund (reversal of tuition) will be provided after the third day of the semester for any class.
You can track your progress toward earning your degree using the Degree Works feature in your MySanDiego student portal. Degree Works shows you which courses you have completed, grades, cumulative GPA, any outstanding graduation requirements, and more!
To access Degree Works:
Submitting your petition to graduate is a requirement for every student. About a semester before your final term, you will be reminded by your Program Coordinator to submit your petition to graduate. Once completed, your Academic Coordinator will review your academic record and contact you if there are any outstanding requirements or issues.
If you are planning on participating in the commencement ceremony (which means walking in your cap and gown here on campus), you will be invited to come to the University of San Diego to participate in the ceremony. Commencement details and information will be sent from your Student Success Team around the month of February.
The Registrar will process their final audit of the degrees 6-8 weeks after grades are posted for your final semester. Once the degree is conferred in the system, the Registrar will order your diploma from the vendor and the vendor will send it to you directly to the address that was listed on your petition to graduate.
The 30 unit program will consist of ten courses. Courses will be offered year-round with three semesters every year; Spring, Summer, and Fall. Each semester will last 14 weeks. Students will take two courses per semester. Courses will run for seven weeks each with a one or two week break in between semesters. This intensive format will allow students to focus on one course at a time and to still complete the degree program in 20 months.
This course is an introduction to probability and statistical concepts and their applications in solving real-world problems, as well as an introduction to coding in Python. This introductory course provides a solid background in the application of probability and statistics that will form the basis for advanced AI methods. Statistical concepts, probability theory, random and multivariate variables, data and sampling distributions, descriptive statistics, and hypothesis testing will be covered. In addition, the use of Python for the performance of basic statistics will be covered in this course. Covered topics include the numerical and graphical description of data, elements of probability, sampling distributions, probability distribution functions, estimation of population parameters, and hypothesis tests. This course will combine the learnings from texts, case studies, and standard organizational processes with practical problem-solving skills to present, structure, and plan the problem as it would be presented in large enterprises and execute the steps in a structured analytics process. Team collaboration, professional presenting, and academic writing will be covered as well through a final team project.
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.
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.
This course is focused on understanding a variety of ways to represent human language as computational systems and how to exploit those representations to develop programs for translation, summarization, extracting information, question answering, natural interfaces to databases, and conversational agents. This course will include concepts central to Machine Learning (discrete classification, probability models) and to Linguistics (morphology, syntax, semantics). Students will learn computational treatments of words, sounds, sentences, meanings, and conversations. Students will understand how probabilities and real-world text data can help. The course covers some high-level formalisms (e.g., regular expressions) and tools (e.g., Python) that can greatly simplify prototype implementation. Students will learn techniques to address the social impact of natural language processing, such as demographic bias, exclusion, and overgeneralization.
This course provides an introduction to computer vision. Computer vision uses a combination of traditional AI, machine learning, image processing, and mathematical theories to provide ways of programming a computer to understand visual imagery, whether a static picture, stereo vision for a robot, or motion from video. Topics covered include fundamentals of feature detection and extraction, motion estimation and tracking, image processing, and object and scene recognition. Students will learn fundamental concepts of computer vision as well as gain hands-on experience in solving real-world vision problems. A variety of tools will be introduced in this course, but the main focus will be on Python and OpenCV, as well as TensorFlow and Keras.
Recent advances in smart devices and technologies have enabled cars, smartphones, TVs, refrigerators, and several other devices to be connected to each other to build, operate, and manage the physical world. The Internet of Things (IoT) has significant potential to impact how individuals live and work by providing the tools necessary for innovative decision-making. The application of AI in IoT requires an understanding of machine learning algorithms, sensors, networking, and data analytics. To prepare our students as forerunners in AI, this course will introduce and practice a wide range of topics in the broad areas of IoT and data analytics and provide hands-on learning experiences and real-world applications. In addition, students will acquire knowledge of the ethics and law in IoT-enabled systems. Concepts in IoT ethics, such as data security, privacy, trustworthiness, and transparency of data, will be discussed in detail.
This course will examine some of the issues and consequences for humanity and our environment of increasing use of Artificial Intelligence (AI) and related technologies. With an understanding of the range of possible issues arising from AI, this course covers and explores how researchers, product teams, and policymakers might address the issues. Students will investigate how processes for AI development and deployment could be adapted to operate more effectively within legal frameworks and satisfy safety goals. This course discusses the social, political, and economic effects that AI may have on society – today and in the future. It also covers developing an understanding of public concerns with AI, including economic, equity, and human rights. Students will review proposed regulations, such as ones that provide individuals with a right to explanation when decisions made by an AI agent affect them. Students will evaluate existing and proposed techniques for addressing known challenges such as fairness, privacy, and liability. In addition, students will apply what they learn by adapting how practitioners work and lead in organizations that create and deploy AI-enabled systems, products, and services. Taken together, students will study and practice ways to ensure that they are equipped to ethically and safely build systems with an artificial intelligence component.
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 assist 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.
In this course, students learn how the knowledge and skills acquired in the Master's program can be directly applied to develop AI-enabled systems. Students will apply skills acquired in the program to effectively address ethical, moral, and social issues in their design process. Students work in teams and participate in the identification of a problem, develop a project proposal outlining an approach to the problem’s solution, implement the proposed solution, and test or evaluate the result in this Capstone using tools and technologies that were taught through the entire program.
This list is helpful resources that will set you up for success. Haven’t written in APA formatting since your undergraduate program? We’ve got you covered! Want to know what type of computer you will need? No problem. We have listed helpful resources below.
System Requirements
Program Software Requirements
Program Software Requirements and Resources
All writing assignments must be formatted according to APA standards. Discussion posts must contain the appropriate APA citations. If you want additional writing support, we recommend Purdue Online Writing Lab (OWL@Purdue). In addition to general writing support, the website includes a special section dedicated to APA formatting guidelines.
Another helpful writing resource is the School of Leadership and Education Sciences (SOLES) Graduate Student Writing Center. Enrolled students can submit assignments for review by a writing professional.
Students at the University of San Diego are able to download Microsoft Office 365 for free! If you don’t have it already, you can download the Microsoft Office 365 suite using your USD student email.
TimelyCare is a provider of 24/7, no-cost telehealth services for USD students to address common conditions that can be safely diagnosed and treated remotely. TimelyCare services are available at no cost to the student. Services include:
Each time your course starts, you will be invited to join a Slack messaging channel for your MS-AAI program. In the channel, you'll be able to easily message your peers and faculty in a dynamic way. New to Slack? No problem - there are a variety of introductory resources to get you up to speed.
Here are some free recommended resources to supplement the course content in your program:
Paid for Resources
The handbook is where you can find information on academic expectations, drop and refund policy, technology requirements, curriculum, frequently asked questions, and more.
USD’s Case Management team has compiled a list of on-campus (local to San Diego) and national economic resources to help students find assistance with a variety of life aspects such as housing, food, mental health, parenting, etc. If you have a need or concern that isn’t addressed by the resources included in this list, please reach out to your Program Coordinator for further assistance. Based on the support, we may refer you to schedule an appointment with a case manager.
It is the policy of the University of San Diego to adhere to the rules and regulations as announced in this brochure or other University Publications. The University nevertheless hereby gives notice that it reserves the right to expand or delete or otherwise modify this online publication whenever such changes are adjudged by it to be desirable or necessary. Changes will be made periodically as needed.
USD welcomes first-generation college and first-generation graduate students. Please view the resources below to connect with your first-gen peers!
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Please view the USD information trifold for current and prospective students. It is also available in Spanish.
Review the Higher Ed Immigation Portal website which includes some useful information and connections to the undocumented student community, state and national policies, information on graduate and professional school access, retention and success. It is a living document that will be updated regularly and include any new resources that campuses and organizations develop.
Many students who attend USD are also parents. USD offers the following resources:
With the rise of AI writing assistants, students must ensure that they use this new technology ethically and honestly. Consult this document for guidance.
The USD Campus is available to all of our students and includes these additional resources.
In your program, you can think of Canvas as your virtual tool to share information with professors and peers. You will use Canvas to access your course content, find course syllabi, review your assignments, and more. Be sure to use your USD credentials to log in. If you have any difficulty logging into your course, be sure to contact ITS at (619) 260-7900 or [email protected].
The concept of netiquette covers proper communication online. Read our guidelines to help cultivate a supportive and productive online environment.
At USD, you join a community of individuals who are all committed to one common goal: your success. As you familiarize yourself with your team, take the opportunity to virtually meet and connect with the resources available to you as a student. Click on the profiles below to learn more about each office or staff member and watch a brief video about their role in supporting you through graduation.
Whether you're hoping to find a new job or earn a promotion, USD has a wealth of resources available to prepare you for your dream role.
Tuition for the MS-AAI program is $965 per unit.
Tuition amounts shown on this website, or in other university publications or web pages, represent tuition and fees as currently approved. However, the University of San Diego reserves the right to increase or modify tuition and fees without prior notice and to make such modifications applicable to students enrolled at USD at that time as well as to incoming students. In addition, all tuition amounts and fees are subject to change at any time for correction of errors. Please note that the displayed tuition covers only the cost of courses, and additional expenses such as books and other fees are not included.
The MS-AAI program is a total of 30 units.
Students will enroll in two prescribed courses each semester for a total of 6 semester units.
The University of San Diego considers 9 or more units as full-time student status. Your program is designed to be part-time, and students enroll in just 6 units per semester. There is no full-time option for this program.
All students are manually enrolled each semester by the USD Student Success team. If you are not able to enroll for a term or if you need to drop your courses, it is your responsibility to notify your Program Coordinator. All students will be held to the respective drop deadlines and refund schedule detailed in your Student Handbook.
Login to your my.sandiego.edu student portal. Under the “My Student Account” tab, review the tutorials for directions on how to view and pay your bill, set up a payment plan, and enroll in eRefund (Direct Deposit). View the “Tuition and Payment Methods” on your Student Success Center for further details.
Log into your my.sandiego.edu student portal and navigate to the “Torero Hub” section on the sidebar. Click on the “My Academics” tab and locate the “View My Grades” link in the top-middle section. Alternatively, you can view your program progress at a glance using the “Degree Works” link.
If you notice a grade inconsistency between Canvas and your MySanDiego portal, please email your instructor to verify what the final grade should be. Your instructor has the ability to update the posted grade.
Log into your my.sandiego.edu student portal and then use the “Degree Works” link to view your degree audit.
You can find the “Degree Works” link in the Torero Hub under the “My Academics” page. If you are interested in requesting a tailored degree plan, please email [email protected].
If you need to take time off from your program, please email your Program Coordinator or the Student Success team at [email protected]. Since you have submitted your enrollment commitment, our team will automatically register you in courses each term unless you have previously notified the team about taking a break.
To order your official, unofficial, or e-transcript(s), view the transcript ordering options page. Otherwise, you can view unofficial/order official transcripts through your MySanDiego portal. Under the “Torero Hub” sidebar option, click on the “My Academics” page, then click on “Request Official Transcript” under the “My Classes” section.
Congratulations on finishing your program! Diplomas are mailed about 6-8 weeks after the degree requirements have been met and processed. Diplomas are mailed to the current address on file at the time degree requirements are completed. (To check your address information, login to your my.sandiego.edu student portal and view your personal information under My Torero Services.)
You will first be emailed a copy of your e-diploma from Parchment prior to receiving your mailed physical diploma.
Throughout your program and after graduation, your Student Success team is here to help! We recommend contacting your Program Coordinator directly, but you can also email our team address at [email protected].
In addition to our team, your Academic Director is a great resource!
All writing assignments must be formatted according to APA standards. Discussion posts must contain the appropriate APA citations. If you are unfamiliar with APA formatting, or simply require additional writing support, we recommend referencing the Purdue Online Writing Lab (also called OWL@Purdue). In addition to general writing support, the website includes a special section dedicated to APA formatting guidelines.
To further support your writing, we highly recommend using the School of Leadership and Education Sciences (SOLES) Graduate Student Writing Center. Students are encouraged to submit written course assignments via the digital submission form for online feedback from a professional writing coach. See site for details.
This course moves very quickly, and it is important that you turn in all assignments on or before their due dates. If, because of an emergency, you have missed a week or more of course work, please contact your professor immediately to inform them. While there is no guarantee that you will be allowed to make up your work, informing your professor early is the best way to get back on track and finish your course successfully.
Please do not wait more than a week without informing your professor. If your instructor’s email is not already visible on the Canvas course, please use the USD directory to find their contact information.
The course surveys are an opportunity to give your feedback on the course assignments, instructors, pacing, workload, learning management system, accessibility, etc. The feedback is reviewed by school leadership and used to determine how courses should be improved for future iterations. All surveys are completely anonymous – which is why your instructors have to make public announcements asking for everyone to submit their surveys. Feedback in the survey will not affect your final grade.
Course surveys traditionally open during the final weeks of the course and close before final grades are posted. The instructors and USD Student Success Team will remind you to complete these surveys for each class. Your program appreciates the time you take to improve the student experience!
The 7-week courses traditionally follow a weekly pattern with three important days:
Please note that there may be some exceptions to this structure. Always refer to the syllabus for deadline details. Please contact the course instructor with any questions.
No, it is uncommon for institutions to offer Latin honors to graduate students because graduate programs already assume a high level of academic achievement and rigor. The idea is that earning a graduate degree itself signifies excellence, making additional distinctions unnecessary: graduate students are held to a high academic standard, and successful completion of a graduate program inherently reflects significant achievement. For this reason, most institutions, including USD, do not offer Latin honors for graduate students.
If you are looking for a way to highlight your high academic achievement at USD, we recommend including your GPA on your resume, LinkedIn, etc.