38 courses with the subject AAI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Introduces practical knowledge of artificial intelligence tools, focusing on chatbots and conversational AI. Examines how these tools operate, emphasizing effective and ethical use across diverse applications. Incorporates applied analysis of AI’s capabilities, limitations, and impacts on education, work, and society.
Offers elective credit for courses taken at other academic institutions.
Builds strategic, technical, and critical literacy in artificial intelligence to prepare professionals to lead responsible AI adoption in business contexts. Emphasizes applied learning. Examines organizational AI readiness, analyzes real-world cases, and explores methods to assess and mitigate risks. Engages with AI tools to evaluate capabilities, limitations, and ethical considerations for informed business decision making.
Introduces the principles and practices of prompt engineering, offering AI users without programming expertise an opportunity to leverage artificial intelligence for diverse tasks. Focuses on designing, refining, and evaluating effective prompts for AI systems that generate high-quality content across varied applications. Examines foundational and advanced prompting frameworks applied in creative, analytical, and professional contexts. Emphasizes responsible and ethical prompt design and the critical evaluation of AI-generated outputs.
Offers elective credit for courses taken at other academic institutions.
Offers elective credit for courses taken at other academic institutions.
Offers elective credit for courses taken at other academic institutions.
Explores practical skills and critical judgment for effective AI use in professional settings. Examines the capabilities and limitations of current AI systems, structured techniques for improving results, and common failure modes. Emphasizes clear communication about AI across varying levels of technical expertise. Introduces foundational concepts and applied perspectives in artificial intelligence. No technical background required.
Reviews the mathematical knowledge required to succeed in advanced artificial intelligence studies. Covers a wide range of mathematical concepts including introductory linear algebra, probability, statistics, and calculus. Beginning with basic linear algebra, introduces vectors, matrices, and linear transformations, which are crucial in understanding data representations and transformations in AI algorithms. Continues with probability and statistics, equipping students with the tools to analyze data, make predictions, and understand uncertainties in applied machine learning. Covers the essentials of calculus, which are fundamental to understanding optimization problems in AI algorithms.
Studies foundational programming and its significance in artificial intelligence applications, recognizing AI's growing influence in various fields. Emphasizes essential programming skills related to data structures and object-oriented programming, the skills crucial for efficiently implementing the applied machine-learning models. Offers students an opportunity to obtain the software skills necessary for conducting fundamental statistical analyses, predictive modeling, and data visualization.
Examines the research methodologies and scientific writing techniques specific to the field of artificial intelligence. Offers students experience conducting, writing, and critically evaluating AI research, to discern groundbreaking studies from less impactful studies. Topics include a diverse array of research methodologies designed for AI and evaluation of the most appropriate methods aligned with student research questions. Reviews the processes of drafting research papers, including writing abstracts, literature reviews, methodologies, results, and discussions tailored to the AI community.
Introduces nontechnical professionals to foundational concepts and organizational applications of artificial intelligence in areas such as human resources, nonprofit management, logistics, and leadership. Examines how technologies including machine learning, natural language processing, and predictive analytics influence decision making, operations, and workforce dynamics. Explores ethical considerations, strategic adoption, and applied case studies that highlight opportunities and challenges in organizational contexts. Emphasizes evaluation of AI approaches, collaboration with technical teams, and leadership perspectives on responsible integration.
Introduces artificial intelligence, tracing its historical evolution and foundational principles. Delves into search algorithms for problem solving, knowledge representation, reasoning, planning, and decision making. Also covers probabilistic learning and the foundational aspects of machine learning. Emphasizes real-world applications to offer students an opportunity to obtain an in-depth understanding of AI's transformative potential, paving the way for more advanced studies in the field.
Analyzes machine learning techniques, methodologies, and best practices tailored for advanced artificial intelligence applications. Covers advanced supervised and unsupervised learning, deep learning architectures, reinforcement learning, and domain-specific applications. Explores crucial topics of ethics, privacy, and fairness in machine learning.
Prerequisite(s): AAI 6610 with a minimum grade of C-
Prerequisite(s): AAI 6610 with a minimum grade of C-
Offers an advanced exploration of the theoretical foundations, architectures, and techniques essential for the design and optimization of deep learning models. Broadly discusses convolutional and recurrent neural networks, attention mechanisms, generative models, and transformer networks. Offers hands-on experience in implementing, optimizing, and deploying deep learning solutions to real-world problems. Highlights current challenges and the latest trends in the field.
Designed to provide a comprehensive understanding of recommender systems and hands-on experience in building personalized recommendation engines. Examines the fundamental concepts of recommendation algorithms, data preprocessing, model evaluation, and deployment strategies. Offers real-world projects, offering the opportunity to create effective and scalable recommendation systems catering to diverse application domains.
Prerequisite(s): AAI 6600 with a minimum grade of C-
Prerequisite(s): AAI 6600 with a minimum grade of C- ; AAI 6610 with a minimum grade of C-
Prerequisite(s): AAI 6600 with a minimum grade of C- ; AAI 6610 with a minimum grade of C- ; AAI 6620 with a minimum grade of C- ; AAI 6630 with a minimum grade of C- ; AAI 6640 with a minimum grade of C- or (EAI 6600 with a minimum grade of C- ; EAI 6610 with a minimum grade of C- ; EAI 6620 with a minimum grade of C- ; EAI 6630 with a minimum grade of C- ; EAI 6640 with a minimum grade of C- )
Prerequisite(s): AAI 6600 with a minimum grade of C-
Prerequisite(s): AAI 6620 with a minimum grade of C- ; AAI 6630 with a minimum grade of C-
Prerequisite(s): AAI 6680 with a minimum grade of C-
Prerequisite(s): AAI 6600 with a minimum grade of C- ; AAI 6610 with a minimum grade of C-
Prerequisite(s): AAI 6710 with a minimum grade of C-
Prerequisite(s): AAI 6710 with a minimum grade of C-
Introduces reinforcement learning, a subset of machine learning that focuses on training agents to make decisions with the aim of achieving specific objectives. Emphasizing practical experience, RL methods can be utilized to address real-world problems, including tasks related to design and optimization. At its core, RL revolves around agents interacting with their environments, learning from rewards and punishments, and continuously improving their decision-making skills. Emphasizes the adaptability of RL across various domains and how, with practical RL expertise and algorithms, RL can effectively address a wide array of industry challenges, ultimately enhancing its applicability and success in various professional settings.
Introduces the integration of quantum computing and artificial intelligence. Explores the fundamentals of quantum algorithms and their applications to AI. Offers students an opportunity to obtain an understanding of the potential and challenges of quantum AI.
Explores extensively the integration of artificial intelligence and blockchain technologies. Offers students an opportunity to obtain a comprehensive understanding of blockchain fundamentals and the core principles of AI and to stay current with emerging trends in the ever-evolving field of blockchain AI. Underscores how these technologies complement each other, with blockchain enhancing security and trust while AI allows intelligent decision making. Examines crucial aspects such as security, privacy, and ethics using real-world case studies, ensuring a well-rounded AI approach.
Explores the essentials of artificial intelligence and its practical applications in autonomous systems. Focuses on vital AI techniques such as machine learning, computer vision, and reinforcement learning. Offers students an opportunity to obtain expertise, to become independent decision makers, and to exercise innovation and control in such systems. Emphasizes hands-on learning involving real-world projects in perception, planning, control, and human-machine interactions while cultivating a deep understanding of AI technologies for autonomous systems. Projects in the course explore system efficiency and capabilities and study technology development across various industries.
Offers elective credit for courses taken at other academic institutions.
Prerequisite(s): AAI 6600 with a minimum grade of C- ; AAI 6610 with a minimum grade of C- ; AAI 6620 with a minimum grade of C- ; AAI 6630 with a minimum grade of C-
Covers special topics in artificial intelligence research. Topics vary by semester. May be repeated up to three times for a maximum of 15 semester hours.
Examines techniques for strategically leveraging generative AI systems to drive organizational productivity, innovation, and competitive advantage. Offers students an opportunity to develop competencies in prompt engineering, AI workflow optimization, and the design of human-AI collaboration frameworks at enterprise scale. Students design and evaluate comprehensive AI implementation strategies, develop organizational policies for responsible AI use, and create original frameworks for integrating generative AI across business functions. Utilizes critical analysis of case studies and academic literature to synthesize best practices from successful AI implementations and apply evidence-based insights to complex organizational challenges. Emphasizes the professional leader's role in maximizing AI effectiveness while managing quality, accuracy, and ethical considerations in real-world business contexts.
Examines frameworks and practices for establishing responsible, ethical, and transparent artificial intelligence systems within organizational contexts. Explores competencies in AI governance design, risk management, regulatory compliance, and ethical decision-making frameworks. Offers students an opportunity to synthesize best practices from regulatory environments and apply evidence-based approaches to complex AI governance challenges. Emphasizes the professional leader's role in building trustworthy AI systems that align with organizational values, legal requirements, and societal expectations while managing risks across the AI life cycle.
Examines strategies for developing workforces capable of thriving in AI-augmented environments and establishing effective human-AI collaboration frameworks. Offers students an opportunity to develop competencies in designing complementary human-AI team structures, creating organizational learning initiatives for AI readiness, and fostering cultures that embrace technological change while preserving human creativity and judgment. Explores workforce development programs, human-AI interaction models, and frameworks for maintaining human agency in increasingly automated contexts. Utilizing critical analysis of case studies and academic literature, synthesizes best practices for reskilling initiatives and applying evidence-based approaches to successful human-AI partnerships. Emphasizes the leader's role in building adaptive, resilient workforces that leverage AI capabilities while cultivating uniquely human skills.
Examines strategies for leading AI initiatives across organizations from an executive perspective. Focuses on strategic decision making, vendor evaluation, and measuring business impact rather than technical implementation. Offers students an opportunity to develop competencies in assessing AI opportunities, making strategic technology decisions, managing vendor relationships, and evaluating AI initiative success. Explores AI adoption road maps, frameworks for selecting and evaluating AI solutions, and business cases that translate AI investments into measurable outcomes. Emphasizes the business leader's role in driving AI initiatives, managing organizational change, and ensuring AI investments deliver sustainable competitive advantage through governance, resource allocation, and performance measurement systems. 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Source: Northeastern University's catalog, linked per course · table learning_unit · CourseShelf publish 61
