ARIN 5300 introduces students to the fundamental concepts, techniques, and applications of artificial intelligence (AI). Through a combination of lectures, discussions, and hands-on exercises, students will explore the theoretical underpinnings and practical implementations of AI technologies. The course covers a broad range of topics, including problem-solving, knowledge representation, search algorithms, and intelligent agents. Additionally, students will examine the ethical considerations and societal implications of AI advancements. Communicate effectively about AI concepts and applications through written reports and oral presentations. Through ARIN 5300, students will develop a solid foundation in artificial intelligence, laying the groundwork for further study and specialization in advanced AI courses.
ARIN 5301 explores the theory, algorithms, and applications of machine learning, a foundational component of artificial intelligence. The course covers various machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning. Through a combination of lectures, practical assignments, and projects, students will gain hands-on experience in implementing machine learning algorithms, evaluating model performance, and applying machine learning techniques to real-world datasets. Through ARIN 5301, students will develop a strong foundation in machine learning theory and practice, equipping them with the skills and knowledge needed to tackle complex AI problems and contribute to advancements in the field.
ARIN 5302 delves into the theory, architectures, and applications of neural networks and deep learning, advanced techniques within artificial intelligence. The course covers various types of neural network architectures, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Through a combination of lectures, hands-on exercises, and projects, students will gain practical experience in designing, training, and deploying neural networks for tasks such as image recognition, natural language processing, and sequence modeling. Through ARIN 5302, students will develop expertise in neural networks and deep learning, enabling them to design and deploy state-of-the-art AI systems for a wide range of applications.
ARIN 5303 explores the theory, algorithms, and applications of natural language processing (NLP), a subfield of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. The course covers various techniques and methodologies used in NLP, including text preprocessing, syntactic and semantic analysis, sentiment analysis, machine translation, and dialogue systems. Through lectures, hands-on exercises, and projects, students will gain practical experience in building NLP models and applications to process and analyze textual data. Through ARIN 5303, students will develop a comprehensive understanding of natural language processing techniques and their applications, preparing them for careers in areas such as text analytics, conversational AI, and information retrieval.
ARIN 5304 explores the theory, algorithms, and applications of computer vision, a subfield of artificial intelligence focused on enabling computers to interpret and understand visual information from the real world. The course covers various topics in computer vision, including image processing, feature extraction, object detection, image segmentation, and image classification. Through lectures, hands-on labs, and projects, students will gain practical experience in designing and implementing computer vision systems for tasks such as image recognition, object tracking, and scene understanding. Through ARIN 5304, students will develop a comprehensive understanding of computer vision techniques and their applications, preparing them for careers in fields such as autonomous vehicles, medical imaging, surveillance systems, and augmented reality.
ARIN 5305 explores the theory, algorithms, and applications of robotics, a multidisciplinary field within artificial intelligence that involves designing, building, and programming autonomous machines capable of interacting with the physical world. The course covers various topics in robotics, including robot kinematics, dynamics, motion planning, perception, and control. Through lectures, laboratory exercises, and hands-on projects, students will gain practical experience in designing and implementing robotic systems for tasks such as manipulation, navigation, and human-robot interaction. Through ARIN 5305, students will develop a comprehensive understanding of robotics principles and techniques, preparing them for careers in fields such as industrial automation, autonomous vehicles, healthcare robotics, and service robotics.
ARIN 5306 delves into reinforcement learning, a subfield of artificial intelligence focused on training agents to make sequential decisions in uncertain environments to maximize cumulative rewards. The course covers various reinforcement learning algorithms, including Q-learning, policy gradients, and deep reinforcement learning techniques. Through lectures, coding assignments, and practical exercises, students will gain hands-on experience in implementing reinforcement learning algorithms, solving Markov decision processes (MDPs), and applying reinforcement learning to control tasks and games. Through ARIN 5306, students will develop a comprehensive understanding of reinforcement learning techniques and their applications, preparing them for careers in fields such as robotics, autonomous systems, game AI, and finance.
ARIN 5307 explores the theory, techniques, and applications of data mining and big data analytics, essential components of artificial intelligence aimed at extracting knowledge and insights from large and complex datasets. The course covers various topics, including data preprocessing, pattern recognition, clustering, classification, and association analysis. Through lectures, hands-on exercises, and projects, students will gain practical experience in applying data mining algorithms to real-world datasets and leveraging big data technologies to analyze and interpret large-scale data. Through ARIN 5307, students will develop a comprehensive understanding of data mining and big data analytics techniques and their applications, preparing them for careers in fields such as business intelligence, data science, and predictive analytics.
ARIN 5308 explores the principles, algorithms, and applications of evolutionary and cognitive computing, which are branches of artificial intelligence inspired by biological processes and human cognition. The course covers various topics, including genetic algorithms, evolutionary strategies, swarm intelligence, neural-symbolic systems, and computational neuroscience. Through lectures, hands-on exercises, and projects, students will gain practical experience in designing and implementing evolutionary and cognitive computing algorithms to solve optimization problems, pattern recognition tasks, and modeling human-like intelligence. Through ARNI 5308, students will develop a comprehensive understanding of evolutionary and cognitive computing techniques and their applications, preparing them for careers in fields such as optimization, artificial life, robotics, and cognitive science.
ARIN 5309 delves into the ethical, societal, and legal implications of artificial intelligence (AI) technologies, focusing on real-world applications and case studies. The course examines ethical frameworks, principles, and guidelines for the responsible development and deployment of AI systems. Through analysis of case studies and discussions of current AI applications, students will explore topics such as bias and fairness, transparency and accountability, privacy and data protection, autonomy and control, and the societal impact of AI technologies. Through ARNI 5309, students will develop a comprehensive understanding of the ethical and societal dimensions of artificial intelligence, enabling them to navigate complex ethical challenges and contribute to the responsible development and deployment of AI technologies in diverse contexts.
ARIN 5310 is the culminating experience of the Master’s Degree in Artificial Intelligence program, where students undertake an independent research project or thesis under the guidance of a faculty advisor. The capstone project gives students an opportunity to apply the knowledge, skills, and techniques acquired throughout the program to address a significant research question or problem in artificial intelligence. Students will define a research topic, conduct a literature review, design and implement experiments or methodologies, analyze data, and present their findings in a formal written thesis and oral defense. Through ARIN 5310, students will demonstrate their ability to conduct independent research, contribute new knowledge to the field of artificial intelligence, and communicate their findings effectively through written and oral presentations. The capstone project serves as a showcase of students’ mastery of AI concepts and methodologies and prepares them for careers in research, academia, or industry roles requiring advanced expertise in artificial intelligence.
Applied Learning Practicum (3 credit hours), offers students the opportunity to put their theoretical knowledge into practice through real-world experiences like internships or work placements. These experiences can take various forms: (1) alternative work/study, internships, cooperative education, (2) employment relevant to the student’s field of study, or (3) collaborative projects with faculty applying coursework to professional contexts. Before beginning their field placement work, students must ensure the University has a Collaborative/Cooperative Agreement with the practicum or internship site, and department approval is necessary to ensure alignment with the program of study. This course is a recurring requirement for all enrolled students, emphasizing its importance as part of executive formatted programs, ensuring students engage with practical applications throughout their academic journey.
