Applied Artificial Intelligence (AAI)
AAI 500 | PROBABILITY AND STATISTICS FOR ARTIFICIAL INTELLIGENCE
Units: 3 Repeatability: No
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.
AAI 501 | INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C-
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.
AAI 510 | MACHINE LEARNING: FUNDAMENTALS AND APPLICATIONS
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 511 | NEURAL NETWORKS AND DEEP LEARNING
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 520 | NATURAL LANGUAGE PROCESSING AND GENAI
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 521 | APPLIED COMPUTER VISION FOR AI
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 530 | DATA ANALYTICS AND INTERNET OF THINGS
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 531 | ETHICS IN ARTIFICIAL INTELLIGENCE
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 540 | MACHINE LEARNING OPERATIONS
Units: 3 Repeatability: No
Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-
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.
AAI 550 | NEW STUDENT ORIENTATION
Units: 0 Repeatability: No
This orientation course introduces students to the University of San Diego and provides important information about the MS-AAI program and the technologies that will be used throughout the program. In the orientation, students will learn to successfully navigate through the online learning environment and locate helpful resources. Students will practice completing tasks in the learning environment as preparation for success in their online graduate courses. This orientation course will be available to students as a reference tool throughout the entirety of the program.