Applied Artificial Intelligence (AAI)

Courses

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. This prerequisite course provides a solid background in the application of probability and statistics that will form the basis for advanced data science methods. Statistical concepts, probability theory, random and multivariate variables, data and sampling distributions, descriptive statistics, and hypothesis testing will be covered. The use of computer-based applications for the performance of basic statistics will be utilized. 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.

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, deep 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 and deep learning 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 is an interdisciplinary field that combines techniques from statistics, linear process big data at a high speed to make predictions or decisions without human intervention. Machine learning applications include business intelligence, biomedical systems, security, and automation. This class will introduce students to the fundamental concepts and algorithms for machine learning. We will learn supervised learning and unsupervised learning techniques such as hidden Markov models, support vector machines, clustering, and dimensionality reduction. Students will acquire skills and knowledge on incorporating ethical issues in machine learning. Students will learn concepts such as dehumanization effects and amplification of human biases that are transferred into training data affecting machine learning.

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, we'll learn the fundamental principles and concepts of neural networks and state-of-the-art approaches to deep learning. Students will learn to design neural network architectures and training methods using hands-on assignments. Students will read current research articles to appreciate state-of-the-art approaches and real world applications. We will learn and use a critical software tool for modern deep learning: TensorFlow.

AAI 520 | NATURAL LANGUAGE PROCESSING

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). We’ll learn computational treatments of words, sounds, sentences, meanings, and conversations. We’ll understand how probabilities and real-world text data can help. We’ll explore state-of-the-art approaches to applications such as translation and information extraction. We will introduce 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 | INTRODUCTION TO COMPUTER VISION

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. Topics covered include fundamentals of image formation, camera imaging, feature detection and extraction, motion estimation and tracking, image processing, object and scene recognition. Students will learn fundamental concepts of computer vision as well as hands on experience for solving real world vision problems. Students will acquire knowledge on applying ethically responsible techniques and fairness in computer vision applications.

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 great potential to impact how individuals live and work by providing a source of innovative decision making. The design of the IoT requires the understanding of software, sensors, network, and data analytics. To prepare our students as forerunners in AI, this course will introduce a wide range of topics in the broad areas of IoT and data analytics and provide hands-on learning experiences and real world applications. Students will acquire knowledge on 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 of increasing use of artificial intelligence (AI) and related technologies. We will consider a range of possible problems arising from AI and how researchers and policy makers might address them. We will investigate how AI could be adapted to operate within safety, ethical, and legal limits. We will also study the economic and social effects that AI could have on society. The course will also consider some legal and policy issues related to the use of AI systems, including fairness, privacy, and liability. We will learn proposed regulations that provide individuals with a right to explanation when decisions made by an AI agent affect them. We will also understand the importance of ethical considerations for guiding computer scientists and engineers who create AI enabled technologies. Economic issues concerning AI will also be examined to understand the unemployment threat caused by the replacement of workers by AI systems and the consequent effects on economic inequality. We will study the challenges of ensuring that artificial intelligent systems and agents behave ethically, legally and safely.

AAI 540 | APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND BIG DATA

Units: 3 Repeatability: No

Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-

In this course, we will learn the current state-of-the art and future directions in applications of AI and big data. Several real world applications of AI will be introduced. These include biomedicine, healthcare, robotics, computer vision, smart homes, and social good. For these areas, we will explore the fundamental challenges, principles, and potential opportunities for future research. We will learn the fundamentals of big data, existing big data platforms and tools, data privacy and data visualization. We will explore several applications of big data in finance, multimedia, health, and social data. We will learn how AI can be a powerful force for social good. Specific applications and issues such as healthcare delivery, sustainability, addressing bias, social and economic justice will be explored in detail.

AAI 541 | CAPSTONE PROJECT

Units: 3 Repeatability: No

Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-

In this course, students learn how the knowledge and skills acquired in the Masters 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.

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 program. Throughout the orientation, students will learn to successfully navigate through the Blackboard learning environment and locate helpful resources. Students will practice completing tasks in Blackboard 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 your program.

AAI 594 | SPECIAL TOPICS IN ARTIFICIAL INTELLIGENCE

Units: 3 Repeatability: Yes (Repeatable if topic differs)

Prerequisites: AAI 500 with a minimum grade of C- and AAI 501 with a minimum grade of C-

This is a special topics course discussing areas of interest in artificial intelligence. This course may be repeated for credit with a different topic.