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The Master of Science in Applied Artificial Intelligence (AI) at SUMMA University prepares professionals to design and develop innovative artificial intelligence solutions by integrating advanced data analysis, automation, and machine learning techniques. The program balances solid theoretical foundations with practical applications focused on solving real-world problems across diverse technological and business sectors, strengthening graduates’ ability to create strategic value and measurable impact through the effective use of AI.
Program Credit Hours
36 Credit hoursEstimated Completion Time
12 monthsThe Master of Science in Applied Artificial Intelligence (AI) is designed to equip students with cutting-edge knowledge and skills in artificial intelligence to address real-world challenges in diverse industries. The program emphasizes a strong foundation in machine learning, data transformation techniques, time series analysis, natural language processing, and image processing. Students gain expertise in leveraging AI platforms, developing open-source applications, and implementing advanced algorithms to create innovative solutions.
The Master of Science in Applied Artificial Intelligence (AI) prepares graduates to utilize advanced tools for data collection, processing, and analysis; apply natural language processing and image recognition techniques; automate processes and develop AI-driven platforms; and innovate in the application of machine learning algorithms across fields such as healthcare, finance, and technology. The curriculum fosters interdisciplinary collaboration and ethical practices while promoting a culture of innovation, encouraging adaptability, critical thinking, and effective communication in dynamic and evolving technological environments. Through the MAS® Social Learning Model, graduates emerge as professionals who combine technical expertise with leadership, responsibility, and a commitment to driving organizational transformation through AI.
The learning methodology is based on the "case method", a virtual course with interactive screens, virtual lectures, videos of the teacher, virtual review sessions and interactive exercises.
You will have weekly work planning and the personalized monitoring of an academic mentor. The teaching staff is made up of PhDs from the world of business and academia.
Applied artificial intelligence has become a key capability for organizations seeking to improve decision-making, automate processes, and develop intelligent digital solutions. The Master of Science in Applied Artificial Intelligence (IA) prepares graduates to apply machine learning techniques for effective data analysis and processing, use natural language processing tools for automated text and speech analysis, develop AI-driven solutions for image recognition and classification, automate business and technological processes using advanced AI algorithms, and implement AI platforms and open-source programming for web and software applications.
Some professional opportunities include:
At the end of the course, the student will demonstrate mastery of machine learning algorithms and computer programs to perform complex tasks, using intelligent systems, machine learning, generative artificial intelligence, software and platforms, in order to have a more complete overview of the data, apply search engine algorithms, automate tasks and open a range of opportunities and solutions for organizations in any area.
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At the end of the course, the student will create open source programming languages for web development and computer applications, using variables, interchangeable code modules, variable data types, basic operators, control structures and functions, in order to be more productive for developers, reduce maintenance costs, be used in various projects and develop web services.
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At the end of the course, students will be able to evaluate large volumes of data through the development of algorithms, using techniques and methods of data transformation and evaluation, descriptive statistics and visualization tools, in order to interpret data to predict future behavior, detect patterns and trends, automate different tasks, as well as detect risks and opportunities.
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At the end of the course, the student will demonstrate a solid command of the fundamental principles, techniques and algorithms in the field of machine learning, through machine learning algorithms, reinforcement learning and frameworks, in order to process and analyze the large volumes of data produced by companies, facilitate more accurate decision-making and automatically program the system to solve problems.
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At the end of the course, the student will develop deep learning algorithms to analyze structured and classified data by making predictions, using recurrent neural network architectures, deep learning libraries, deep learning techniques, image recognition, natural language processing, text generation and sequence analysis, with the aim of improving automation, recognizing complex patterns and performing analytical and physical tasks without the need for human intervention.
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At the end of the course, the student will evaluate sets of statistical variables over time periods to try to predict the future values of the series, through observation, analysis of temporal data, identification of patterns and trends over time, statistical models and machine learning techniques, in order to make predictions, solve practical problems and make informed decisions based on temporal data in different contexts.
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At the end of the course, the student will develop the understanding, analysis, processing and interpretation of texts and data, using computational tools, text processing techniques, vector space models, recurrent model architectures and machine translation, with the aim of obtaining large-scale data analysis in a short time, promoting fast and real-time automated processes.
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At the end of the course, the student will apply technological tools in the automated extraction of information from images for facial recognition, model construction, image classification, among others, through advanced image processing techniques, edge detection, object recognition and classification, in order to automatically identify an image of symbols or characters, automate complex tasks and interpret the information and store it as data.
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At the end of the course, the student will use software systems to generate and provide suggestions for specific content in any application or website, using filter-based recommendation systems and development framework tools , in order to detect user needs, increase search results, predict tastes and recommend products.
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At the end of the course, the student will prepare generative artificial intelligence systems for the creation of original content from existing data, using algorithms, generative neural networks, and language models for large and small texts, in order to generate data, create quality content, streamline complex tasks, and innovate ideas and solutions.
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At the end of the course, the student will test artificial intelligence tools in the cloud to provide services and create intelligent web applications for companies and individual users, through the fundamentals of computing and management of the main cloud service providers, with the aim of allowing companies to obtain deeper data, personalize products and services, meet customer needs and improve operational efficiency.
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At the end of the course, the student will develop a master's thesis in the area of Applied Artificial Intelligence, applying the knowledge acquired throughout the training program, carrying out original work, analyzing relevant data and presenting significant conclusions in relation to a specific topic of interest in the computer science sector. This, with the aim of demonstrating the capacity for research, analysis and application of the theoretical and practical concepts acquired, as well as contributing to the advancement of knowledge in the field of Applied Artificial Intelligence, adding value to the organization or sector in which the master's thesis is developed.
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| Price per Credit: | US $168.06 |
| Total Price: | US $6050.00 / 36 credits |
| Registration Fee: | US $100.00 (non-refundable, one-time charge) |
| Graduation Fee: | US $110.00 |
| Return Check Fee: | US $40.00 |
| Official Transcript: | US $10.00 (each copy) |
| Withdrawal Processing: | US $25.00 |
| Books & Materials: | US $0.00 |
| Other costs: | US $0.00 |
8400 NW 36th St #450
Doral, FL 33166
USA
+1 (786) 885-5188
Mon – Fri from 8am until 5pm
Admissions: [email protected]
Academic Coordination: [email protected]
Registrar: [email protected]
Finance: [email protected]
Graduation Services: [email protected]
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