Student Success Center
MS in Applied Artificial Intelligence
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Welcome!
As an online student, most of your interactions with the university will be done through various websites. For example:
- You’ll use your USD Email Address for all university-related communications.
- You’ll use Canvas for all of your coursework and assignments. See Canvas Tips and Tricks page.
- You’ll use your MySanDiego Portal for tuition payments and to access most campus resources.
- You’ll use this site, your Student Success Center, to get specific information about textbook information, deadlines, and more!
Stay Connected with your Program
Your Program Coordinator
Ashley Dominguez
(619) 260-7521
Your academic director
Ebrahim Tarshizi, PhD, MBA
(619) 260-8802
COMPLETE YOUR MANDATORY NEW STUDENT CHECKLIST
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.
1. View Your Mandatory New Student Welcome Webinar
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 given the link to schedule your new student check-in call.
2. Schedule Your New Student Check-in Call
In this call, you’ll “meet” a member of your Student Success Team for your program. Once you attend the welcome webinar, you will be given the link to schedule your new student check-in call. 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.
3. Complete Your Canvas Orientation Course – Due May 3, 2024
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?
4. Finalize Your Tuition Payment Plan
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.
5. Review your Student Handbook
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.
Graduate Student Success Program
2024 Dates: July 22-24, 2024
2025 Dates: TBD
The Graduate Student Success Program is an optional opportunity for students to unpack the hidden curriculum of graduate school, connect with other graduate students across the university, and feel empowered to step into the new identity of being a graduate student and scholar. Through workshops and presentations, our virtual program will address questions such as:
- How do I make the most of my experience as a graduate student?
- What if I feel like I don’t belong here?
- How do I interact with faculty as a graduate student?
- How do I tend to my financial wellness while in graduate school?
- What are some strategies to access support?
IMPORTANT DATES
Below is a list of significant dates regarding the registration process, payment deadlines, and other important academic and program deadlines.
Fall 2024 Dates and Deadlines
| Important Dates | Date |
|---|---|
| Application Deadline | August 2, 2024 |
| Registration Opens | July 1, 2024 |
| Registration Deadline | August 16, 2024 |
| Orientation Course Due Date | August 30, 2024 |
| Last Day to Drop with 100% Refund | September 2, 2024 |
| Payment Due Date | September 3, 2024 |
| Semester Begins | September 3, 2024 |
| First Course Start Date | September 3, 2024 |
| Last Day to Withdraw from Course A | September 30, 2024 |
| First Course End Date | October 21, 2024 |
| Second Course Start Date | October 22, 2024 |
| First Course Final Grade Submission Due Date | November 4, 2024 |
| Last Day to Withdraw from Course B | November 18, 2024 |
| Second Course End Date | December 9, 2024 |
| Semester Ends | December 9, 2024 |
| Second Course Final Grade Submission Due Date | December 23, 2024 |
Spring 2025 Dates and Deadlines
| Important Dates | Date |
|---|---|
| Application Deadline | December 2, 2024 |
| Registration Opens | November 1, 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 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 2024 Dates and Deadlines
| Important Dates | Date |
|---|---|
| Application Deadline | April 8, 2024 |
| Registration Opens | March 4, 2024 |
| Registration Deadline | April 19, 2024 |
| Orientation Course Due Date | May 3, 2024 |
| Last Day to Drop with 100% Refund | May 6, 2024 |
| Payment Due Date | May 7, 2024 |
| Semester Begins | May 7, 2024 |
| First Course Start Date | May 7, 2024 |
| Last Day to Drop with 95% Refund/ Drop Deadline | May 10, 2024 |
| Last Day to Withdraw from Course A | June 3, 2024 |
| First Course End Date | June 24, 2024 |
| Second Course Start Date | June 25, 2024 |
| First Course Final Grade Submission Due Date | July 8, 2024 |
| Last Day to Withdraw from Course B | July 22, 2024 |
| Second Course End Date | August 12, 2024 |
| Semester Ends | August 12, 2024 |
| Second Course Final Grade Submission Due Date | August 26, 2024 |
Download a pdf of your academic calendar
Class Registration Directions
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.
Textbook information
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.
Digital Inclusive Access (IA) – Integrated VitalSource Textbooks
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.
- Students are able to access the e-textbook directly in the course without worrying about purchasing the wrong version or textbook!
- If your course is indicated as using “Digital Inclusive Access” on the textbook list, please note that you must opt-out within the first 7 days of each course to avoid being charged for the guaranteed-lowest price textbook.
- If you do intend to use “Digital Inclusive Access”, no further action is required after you create a Vitalsource account.
- No refunds can be issued once the textbook charge has been assessed.
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.
PAYing for your program
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.
Program Tuition
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:
- Per Unit (classes are typically 3-units) = $965
- Per Semester (assuming 6-units per semester) = $5,790
- Total Program Cost (including 5 semesters) = $28,950
Slack Pro
All students are charged a $60 fee annually to cover a Slack Pro Account that allows you to have access to your MS-AAI Slack student and instructor community. Only active students will maintain access to their Slack Pro, and students will lose access to this account upon graduation. This fee cannot be waived for any students.
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 One Stop Center via email at [email protected] or phone at 619-260-2700. All costs and fees are subject to change and are based on the academic year of enrollment.
Refund Policy
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.
DEGREE PROGRESS and COMMENCEMENT
DegreeWorks
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:
- Log in to your MySanDiego student portal.
- Under the “Torero Hub” tab, click on “My Academics.”
- On the right-hand side under “My Academic Resources”, click on “Degree Works.”
Petition to Graduate
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.
Commencement and Diploma Details
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 in the month of May to participate in the ceremony. Commencement details and information will be sent from your Student Success Team around the month of February. Please note, there is only one commencement ceremony each academic year. Fall graduates will be invited to the commencement ceremony the May after they graduate, while Summer graduates will be invited to the commencement ceremony the May before they graduate.
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. Mailing time is an additional 6-8 weeks from the date of order, and you will likely receive your diploma in the mail in 3-4 months after you have completed your degree requirements. *Please note, if your mailing address changes after you submit your Petition to Graduate, please notify the Graduate Records office at (619) 260-2217 or [email protected].
Course Requirements
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.
Course Titles and Descriptions
Probability and Statistics for Artificial Intelligence (AAI 500)
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.
Introduction to Artificial Intelligence (AAI 501)
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: Fundamentals and Applications (AAI 510)
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 and Deep Learning (AAI 511)
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.
Natural Language Processing and GenAI (AAI 520)
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.
Applied Computer Vision for AI (AAI 521)
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.
Data Analytics and Internet of Things (AAI 530)
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.
Ethics in Artificial Intelligence (AAI 531)
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.
Machine Learning Operations (AAI 540)
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.
Capstone Project (AAI 590)
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.
Student Resources
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.
Technical Requirements
System Requirements
- Computer: A PC (recommended) or Mac with 8GB of RAM, an Intel or AMD x86-64 processor with four logical cores and ACX2 instruction set support, and 100GB of free drive space is recommended.
Program Software Requirements
- Operating System
- Windows 10 (version 1803 or higher)
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
- High–speed Internet connection is required (DSL or better).
- Browser Plug-ins – Windows Media Player, QuickTime, Adobe Reader, and Java.
- Google Chrome is the recommended browser.
- Microsoft Office is required. As a USD student, you can download Microsoft 365 (Word, PowerPoint, Excel) for free.
Program Software Requirements
Graduate Writing Expectations and Tips
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.
Download Microsoft Office 365 (Free for USD Students)
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 – Telehealth for USD Students
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:
- Live and on-demand self-care sessions (yoga, breathing, stretching)
- Access to on-demand counselors for emotional health support
- Access to schedule a session with a licensed counselor (up to 12 times per year)
- View the Nurturing Balance Webinar Recording to learn about more resources.
Slack – Your Course Messaging Tool
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.
- Please note that we will invite your @sandiego.edu email address to the Slack channel, and we will delete personal email accounts to maintain program and student privacy.
- While participating in the Slack channel, please be respectful of everyone and note that all USD student conduct policies apply to conversations in the related messaging threads.
- If you are having trouble accessing your Slack channel, please email your Program Coordinator using the information at the top of this website.
Additional Academic Resources
Here are some free recommended resources to supplement the course content in your program:
- Khan Academy: Calculus
- Khan Academy: Statistics
- Math is Fun: Statistics
- Khan Academy: Linear Algebra
- Calculus eTextbook by Gilbert Strang
- LearnPython.org: Free interactive Python tutorial
- DataCamp.com: free interactive Introduction to Python
- USD MS-AAI Blog
Paid for Resources
MS-AAI Student Handbook
The handbook is where you can find information on academic expectations, drop and refund policy, technology requirements, curriculum, frequently asked questions, and more.
USD Economic Resource List
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.
Student Code of Rights and Responsibilities for Graduate Students
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.
First-Generation Student Resources
USD welcomes first-generation college and first-generation graduate students. Please view the resources below to connect with your first-gen peers!
- Visit the USD First-Generation website.
- Free & Virtual First-Generation Graduate Student Symposium.
- Instagram accounts: @usd_fgsa and @usdfirstgen.
- Contact the First-Gen Action Team at [email protected].
Military Student Resources
USD provides student-focused services to all USD military-connected students, including student veterans, active-duty, dependent children, and spouses.
- Get started by requesting your Request to Certify each semester.
- Explore Military Scholarship Opportunities.
- Join the Student Veteran Organization .
- Leverage the partnership between USD and 50strong that brings together employers and veterans.
- Upon graduation, connect with the Alumni Veteran Network
USD Campus Partner Email Opt-Out
Get too many emails? Follow our email opt-out guide to optimize your USD email experience.
DACA/Undocumented Student Resources
- Please view the USD information trifold for current and prospective students. It is also available in Spanish.
- Review this 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.
Academic Standards for Artificial Intelligence (AI)
- With the rise of AI writing assistants, students must ensure that they use this new technology ethically and honestly. Consult this document for guidance.
Canvas @ USD
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].
Netiquette Guidelines
The concept of netiquette covers proper communication online. Read our guidelines to help cultivate a supportive and productive online environment.
your partners toward success
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.
USD Quick Links
- USD Directory (For Faculty or Staff Contact Information)
- One Stop Student Center
- Health and Wellness
- SOLES Graduate Writing Center
- Military and Veterans Program Center
- Disability and Learning Difference Resource Center
- Copley Library
- Career Development Center
- Campus Recreation (Remote and On-Campus Classes Offered)
Career and professional resources
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.