master · School of Business
Master of Science in Artificial Intelligence
An 18-month, 100% online master designed for professionals from any field — no prior programming experience required. Available in Spanish and English.
18 months 40 credits Online
AI for Every Professional
The Master of Science in Artificial Intelligence is designed for professionals from any field who wish to prepare for a work environment increasingly driven by technology and innovation. No prior experience in programming or artificial intelligence is required to join this program. Through a practical and accessible approach, you will learn to use AI tools to analyze data, optimize processes, automate tasks, and make better-informed decisions in your professional field.
Over the course of 18 months, you will develop skills applicable to any industry through projects based on real-world scenarios. The program combines areas such as data science, machine learning, deep learning, cloud computing, and prompt engineering, enabling you to implement innovative solutions that create a positive impact within your organization.
The Only 100% Online U.S. Master in Spanish
Humboldt International University is the only accredited, 100% online university in the United States offering this master's degree entirely in Spanish—designed specifically for Spanish-speaking professionals—with the option to complete the program in English as well. Thanks to its virtual format, you can study from anywhere in the world, earn a degree from an accredited U.S. university and advance without interrupting your professional life.
Upon completing the program, you will be prepared to use Artificial Intelligence as a practical tool to boost your productivity, optimize your daily work, and successfully adapt to the evolving demands of the job market.
Program Learning Outcomes
- Building Knowledge-Based Systems: Students will master different Knowledge Representation Techniques that they can use to design and build Knowledge-Based Systems, in any area of knowledge in their educational or work environment.
- Application of Core AI Concepts: Graduates will demonstrate proficiency in foundational AI principles, including supervised and unsupervised learning, knowledge-based systems, and deep learning techniques, and apply these to solve real-world problems in healthcare, finance, and manufacturing.
- Data Processing and Analysis: Students will learn to collect, clean, and transform large datasets, preparing them for analysis through Python and AI frameworks. They will ensure data quality to support reliable AI model performance.
- Mastery of AI Tools and Frameworks: Graduates can effectively utilize leading programming tools such as Python, TensorFlow, and PyTorch to develop and deploy AI models, including machine learning and deep learning solutions.
- Ethical and Responsible AI: Students will be equipped to critically evaluate the societal impact of AI technologies and apply ethical considerations when designing and implementing AI systems, ensuring transparency, fairness, and accountability.
- Advanced Problem-Solving with AI: The program will foster the ability to innovate and create AI-driven solutions for difficult problems, leveraging techniques like knowledge representation, automated reasoning, and AI-based decision-making processes.
- Industry-Ready Skills in AI: Through practical projects and industry collaboration, students will develop the capability to translate AI theory into practice, delivering solutions that improve business processes, enhance decision-making, and remarkable innovation across various industries.
Program Breakdown by Course
| Course Number | Course Name | Semester Credits |
|---|---|---|
| Core Courses (40 Credits) | ||
| CAI-6005 Introduction to Artificial Intelligence Artificial Intelligence (AI) is a dynamic field of research dedicated to creating computer systems that replicate human intelligence, encompassing reasoning, learning, problem-solving, language comprehension, and sensory perception. This introductory course offers a comprehensive foundation in AI by examining its historical evolution—from early expert systems to the rise of deep learning enabled by large datasets and enhanced computational power. It explores fundamental concepts, tools, and techniques used to model intelligent behavior, such as machine learning, artificial neural networks, expert systems, genetic algorithms, fuzzy logic, natural language processing, and computer vision. Students will learn how to organize knowledge, exploit constraints, navigate complex search spaces, understand natural languages, and apply problem-solving strategies. In addition, the course analyzes the transformative impact of AI across industries like healthcare, finance, education, and government, while addressing ethical considerations and the growing need for governance as AI systems become more embedded in everyday life. | Introduction to Artificial Intelligence | 3.0 |
| CAP-6676 Knowledge Representation Techniques This course offers a fully practical approach to Artificial Intelligence, equipping participants with the skills to design and develop intelligent systems capable of emulating human reasoning for complex decision-making. Starting from foundational concepts and progressing to advanced knowledge-based system (KBS) architectures, students will gain hands-on experience with techniques and tools essential for implementing adaptive AI solutions across diverse domains. Emphasis is placed on the application of various logic forms—predicate logic, first-order logic, non-monotonic logic—as well as procedural representations, semantic networks, production systems, frames, scripts, and other knowledge representation models. A key feature of the course is the use of HAries (Hybrid Artificial Intelligence Expert Systems), a user-friendly, visual programming environment that facilitates the development of practical and robust AI solutions. | Knowledge Representation Techniques | 3.0 |
| CAP-6680 Building Knowledge-Based Systems By the end of the course, students will be able to: Understand and apply the fundamentals of expert systems and artificial intelligence, with a practical focus on designing systems that emulate human reasoning for complex decision-making. They will learn to manage various forms of knowledge and meta-knowledge representation, understanding how these structures help organize and adapt information to different types of problems. Students will also acquire skills to implement logical inference mechanisms using different types of rules, enabling them to structure deduction processes and result presentation in real-world contexts. They will be able to use programming structures within knowledge-based systems (KBS) such as loops, assignments, and interrupts—equivalent to those found in classic programming languages—allowing them to simulate complex and customizable behaviors. Additionally, students will learn to work with specialized tools like HAries, exploring hybrid programming practices in artificial intelligence with a focus on symbolic representation and inference logic. | Building Knowledge-Based Systems | 3.0 |
| CAI-6107 Unsupervised Machine Learning This course focuses exclusively on unsupervised learning techniques, a vital area of artificial intelligence dedicated to discovering hidden patterns, structures, and relationships within unlabeled data. Students will explore key methods such as clustering, dimensionality reduction, anomaly detection, and deep generative models, all aimed at extracting meaningful insights without prior knowledge of data categories. Emphasis is placed on exploratory data analysis, where learners will apply similarity measures and algorithmic strategies to reveal underlying trends in complex datasets. Through a combination of theoretical foundations and practical hands-on projects, the course prepares students to implement unsupervised learning models to address real-world challenges across various domains. | Unsupervised Machine Learning | 3.0 |
| CAI-6105 Supervised Machine Learning This course offers an in-depth exploration of supervised learning techniques in machine learning, where models are trained on labeled datasets to predict outcomes or classify new, unseen data. Students will study key algorithms and models used in both classification—such as decision trees, logistic regression, and artificial neural networks—and regression—including linear regression and other predictive models. Emphasis is placed on understanding how these algorithms learn from data to make accurate predictions, as well as mastering techniques for model evaluation, such as accuracy metrics, confusion matrices, and cross-validation, and model fitting to avoid underfitting or overfitting. Through a balanced blend of theory and practical programming using modern tools, students will gain the skills to design, implement, and optimize supervised learning systems for real-world applications. | Supervised Machine Learning | 3.0 |
| CAI-6220 Mastering Prompt Engineering This course provides students with a comprehensive foundation in prompt engineering, focusing on the core concepts and methodologies essential for working with large language models (LLMs) such as ChatGPT, Claude, Bard, and LLaMA. Students will learn to design, refine, and evaluate prompts using advanced techniques and current best practices to optimize the performance and accuracy of AI-generated outputs. The course emphasizes hands-on application, enabling students to implement their knowledge across various general-purpose AI tools—including chatbots, image generators, presentation creators, and code generators—enhancing productivity and the quality of their work in real-world scenarios. | Mastering Prompt Engineering | 3.0 |
| CAI-6010 Python for Data Processing This course offers an integrated introduction to artificial intelligence (AI), machine learning, and deep learning, while building foundational skills in data science programming using Python. Designed for students from non-computer science backgrounds, the course begins with core AI and machine learning concepts and algorithms, followed by practical applications in data analysis. Students will gain both theoretical knowledge and hands-on experience through coding exercises that emphasize Python syntax, data acquisition, visualization, statistical analysis, and data cleaning. Utilizing powerful Python libraries such as NumPy, Pandas, Matplotlib, and SciPy, the course also introduces advanced topics like graphical user interface (GUI) design for data processing, equipping students with the skills to implement and analyze AI-driven solutions in real-world contexts. | Python for Data Processing | 3.0 |
| CAP-6741 Data Visualization This advanced course in data visualization explores the principles and techniques necessary to transform complex data into meaningful visual insights. Students will investigate the role of visualization across a wide range of disciplines—such as climate science, epidemiology, economics, and social analysis—highlighting its power to uncover patterns, trends, and relationships often hidden in raw data. Through collaborative project work, students will propose, implement, and present data-driven visualizations that demonstrate analytical depth and design proficiency. The course emphasizes exploratory data analysis and the application of Python-based visualization tools, focusing on the selection of appropriate visual formats based on data type, structure, and analytical goals. Visualization is positioned as a core component of the data science workflow, serving as both an investigative and communicative tool for real-world decision-making. | Data Visualization | 4.0 |
| CAP-6752 Data Science This graduate-level course provides a comprehensive foundation in data science and applied artificial intelligence, designed for students in the MSAI program seeking to integrate data-driven methodologies into intelligent systems. Emphasizing real-world applications, students will engage with advanced data analysis, predictive modeling, and data visualization techniques using Python and industry-standard libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib. Through the structured CRISP-DM framework, students will master the end-to-end data science pipeline—from data acquisition and preprocessing to model building, validation, and deployment—while exploring the integration of machine learning and deep learning models for insight generation and automation. The course also emphasizes ethical considerations, stakeholder communication, and the ability to translate complex findings into strategic action. Case studies from domains such as healthcare, finance, cybersecurity, and e-commerce will enable students to apply concepts in diverse contexts, positioning them to lead AI-driven initiatives with both technical proficiency and analytical insight. | Data Science | 4.0 |
| CAI-6210 Deep Learning This graduate-level course offers a comprehensive exploration of deep learning, a vital subset of artificial intelligence centered on neural networks and their transformative role in modern technology. Over eight weeks, students will study the mathematical foundations of deep learning, delve into single and multi-layer neural networks, and apply advanced architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers to real-world problems in areas like computer vision, natural language processing, and autonomous systems. Emphasis is placed on hands-on experience using leading deep learning frameworks to design, train, and evaluate models. Additionally, the course addresses the ethical implications of deploying deep learning systems, encouraging students to develop solutions that are not only technically robust but also socially responsible. By the end of the course, students will be equipped with both the practical skills and critical perspectives necessary to tackle complex AI challenges and drive innovation across industries. | Deep Learning | 3.0 |
| CNT-6425 Cloud Computing This graduate-level course in Cloud Computing Administration offers a comprehensive and hands-on exploration of cloud infrastructure, focusing on both foundational concepts and advanced tools used in modern computing environments. Students will gain in-depth knowledge of virtualization, containers, resource management, programming and application models, and system administration, while working with real-world platforms such as OpenStack, Amazon Web Services (AWS), Microsoft Azure, Google Cloud, as well as big data frameworks like Hadoop and Spark. The course emphasizes student-centered learning by integrating theoretical foundations with practical competencies, including the deployment and management of infrastructure across public and private cloud systems. A significant component is the intersection of Cloud Computing and Artificial Intelligence, where students will learn to design and implement AI-driven solutions using cloud resources, including machine learning workflows. Ethical, legal, and regulatory considerations are addressed through real case evaluations, ensuring students not only master technical tools but also understand the broader societal implications of deploying AI in the cloud. | Cloud Computing | 4.0 |
| CET-6939 Capstone: Master's Final Project This graduate-level Capstone Project course serves as the culminating academic experience in the Master’s in Artificial Intelligence (MSAI) program, providing students with the opportunity to apply their theoretical knowledge to real-world challenges. Through an integrative, project-based approach, students will identify, design, and propose solutions to complex problems within their chosen area of emphasis, using advanced AI techniques such as supervised and unsupervised learning, deep learning, prompt engineering, or AI-driven decision systems. The course emphasizes critical thinking, strategic planning, and applied problem-solving within realistic settings, where problem statements and data originate from industry, government, or non-governmental organizations. Students will engage in the complete process of addressing a real-world AI problem, from data collection and processing to selecting and applying suitable analytical methods. The final deliverable is a comprehensive project proposal encompassing problem analysis, methodology, implementation plans, and ethical considerations, preparing students for practical implementation in professional, research, or entrepreneurial environments. | Capstone: Master's Final Project | 4.0 |