Master of Science in Artificial Intelligence

This curriculum map illustrates the progressive development of students' competencies across the Master of Science in Information Technology Management program, showing how each course introduces (I), develops (D), or brings students to mastery (M) of the seven Program Learning Outcomes (PLOs), culminating in the capstone course where students demonstrate mastery of all outcomes.

• TECH 500: Ethical Challenges in Technology Management
• TECH 650: Foundations of Machine Learning
• TECH 515: Managing Cloud Infrastructure and Security
• TECH 575: Big Data Analytics for IoT
• ISTM 615: Applied AI: Solutions for Business
• TECH 675: Applied Natural Language Processing and Intelligent Text Analysis
• TECH 557: Mobile App Development
• TECH 685: Practical AI Development and Optimization
• TECH 630: Advanced AI for Business Insights and Decision-Making
• TECH 699: Advanced AI Solutions - Capstone Project

Core Classes

TECH 500: Ethical Challenges in Technology Management

This course focuses on preparing leaders to resolve complex ethical dilemmas in technology management. The course emphasizes Biblical values and practical solutions to contemporary challenges. Students explore ethical systems through a Christian worldview, analyze case studies, and develop skills to make sound moral judgments. By the course's end, participants will be equipped to address ethical issues in technology leadership with integrity and a faith-based perspective.

Course Student Learning Outcomes

• SLO 1: Analyze complex ethical dilemmas in technology management using various ethical frameworks, including a Christian worldview. (PLO 4)
• SLO 2: Evaluate the implications of emerging technologies on ethical decision-making in IT leadership roles. (PLO 4, PLO 5)
• SLO 3: Synthesize Biblical principles with contemporary ethical challenges to develop faith-based solutions in technology management. (PLO 4)
• SLO 4: Develop and articulate sound moral judgments for case studies in technology ethics, demonstrating critical thinking and effective communication. (PLO 4)
• SLO 5: Create a personal ethical framework for addressing technology management challenges that integrate professional standards with Christian values. (PLO 4)

TECH 650: Foundations of Machine Learning

This course offers a comprehensive introduction to machine learning, covering theoretical foundations and practical applications across supervised, unsupervised, and reinforcement learning paradigms. Students will explore a wide range of algorithms, including linear and logistic regression, decision trees, support vector machines, neural networks, and clustering techniques while developing skills in data preprocessing, feature engineering, model selection, and performance evaluation. Ethical considerations in AI development are integrated throughout. Students will gain practical experience through hands-on programming assignments and projects using Python and libraries like Scikit-learn, TensorFlow, and PyTorch.

Student Learning Outcomes

• SLO 1: Evaluate the suitability of various machine learning algorithms for complex, real-world problems, demonstrating critical thinking and analytical skills. (PLO 1, PLO 5)
• SLO 2: Synthesize machine learning models that integrate multiple algorithms and techniques to address multifaceted challenges in data analysis and prediction. (PLO 1)
• SLO 3: Create ethically responsible machine learning solutions that consider issues of bias, fairness, and societal impact. (PLO 4)
• SLO 4: Design and conduct rigorous experiments to assess the performance and limitations of machine learning models, demonstrating advanced research and analytical capabilities. (PLO 3)
• SLO 5: Formulate and communicate complex machine learning concepts and results to both technical and non-technical audiences, showcasing advanced communication skills. (PLO 2)

TECH 515: Managing Cloud Infrastructure and Security

This course provides an introduction to enterprise data management and networking technologies in Information Technology (IT), focusing on cloud-based operations and security. Students will explore network technologies, cloud architectures, and data center operations, emphasizing secure IT infrastructures, data protection principles, and operational efficiency in cloud environments. The course also addresses compliance with industry standards and regulations, preparing students to navigate the complex landscape of enterprise IT.

Student Learning Outcomes

• SLO 1: Evaluate complex cloud-based network architectures and design optimal solutions for enterprise-level IT infrastructure. (PLO 3)
• SLO 2: Synthesize best practices in cloud security to create comprehensive risk management strategies, addressing emerging threats and regulatory compliance. (PLO 3)
• SLO 3: Create innovative data management and storage solutions for cloud environments, optimizing for scalability, performance, and cost-effectiveness. (PLO 1)
• SLO 4: Critique IT practices through the lens of Christian stewardship, formulating ethical frameworks for responsible technology utilization in enterprise settings. (PLO 4)
• SLO 5: Predict the impact of emerging trends in cloud computing and enterprise IT on organizational operations, and devise adaptive strategies to leverage these technologies securely. (PLO 5)

TECH 575: Big Data Analytics for IoT

This course introduces Apache Spark, a big data processing framework, focusing on its application in large-scale dataset analysis. Students will leverage Spark's capabilities using Python, covering advanced data manipulation techniques, machine learning applications, and real-world problem-solving scenarios. By the end, students will gain proficiency in Spark for data analysis and machine learning model development.

Student Learning Outcomes

• SLO 1: Synthesize Python programming and Apache Spark frameworks to design and implement advanced big data analysis solutions. (PLO 1)
• SLO 2: Evaluate and apply Spark 2.0 DataFrame syntax to optimize complex data processing tasks and improve analytical efficiency. (PLO 3)
• SLO 3: Create and critique sophisticated machine learning models using Spark's MLlib to solve real-world classification problems. (PLO 1, PLO 5)
• SLO 4: Develop and assess innovative natural language processing applications, such as spam filters, utilizing Spark for text analysis and classification. (PLO 1, PLO 5)
• SLO 5: Formulate an ethical framework for big data analytics that integrates Christian principles of stewardship and privacy, critically examining the societal implications of large-scale data analysis techniques. (PLO 4)

TECH 615: Applied AI: Solutions for Business

This course introduces AI's impact across industries, addressing the growing demand for AI skills. Students will explore deep learning, reinforcement learning, natural language processing, computer vision, and robotics. The curriculum focuses on solving real-world business challenges such as customer churn prediction, image recognition, stock price forecasting, recommender systems, and NLP applications.

Student Learning Outcomes

• SLO 1: Evaluate the impact of AI on various industries, analyzing trends and predicting future developments. (PLO 1, PLO 5)
• SLO 2: Design and implement artificial neural networks to solve business problems like customer churn prediction and stock price forecasting. (PLO 1)
• SLO 3: Develop AI models using convolutional and recurrent neural networks for image recognition and time-series analysis. (PLO 1)
• SLO 4: Create and assess recommender systems and natural language processing applications to enhance customer experience and business operations. (PLO 1, PLO 5)
• SLO 5: Synthesize ethical considerations in AI implementation with Christian principles of stewardship and human dignity, formulating responsible AI strategies for business applications. (PLO 4)

TECH 675: Applied Natural Language Processing and Intelligent Text Analysis

This course explores Natural Language Processing (NLP), a subfield of AI focusing on computer-human language interaction. Students will cover text preprocessing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and machine translation while learning advanced deep learning architectures like RNNs and transformers. The course prepares students to implement innovative NLP solutions across various domains.

Student Learning Outcomes

• SLO 1: Analyze complex NLP algorithms and architectures, demonstrating an understanding of their theoretical foundations and practical implications. (PLO 1)
• SLO 2: Design and implement advanced NLP solutions using tools and libraries to address real-world language processing challenges. (PLO 1)
• SLO 3: Evaluate the performance and limitations of various NLP models and techniques, assessing their suitability for different applications. (PLO 3, PLO 5)
• SLO 4: Develop ethically responsible NLP applications, considering issues of bias, fairness, and societal impact. (PLO 4)
• SLO 5: Synthesize and communicate complex NLP concepts to both technical and non-technical audiences, showcasing proficiency in scientific writing and presentation. (PLO 2)

Institutional Learning Outcomes (ILOs)

• ILO 1: Students will communicate effectively.
• ILO 2: Students will use methods of inquiry for knowledge acquisition and application.
• ILO 3: Students will address concrete problems by applying faith and ethical reasoning.
• ILO 4: Students will think creatively and critically to pursue a life of learning.
• ILO 5: Students will engage in a culturally diverse world to strengthen relationships with others.

Program Learning Outcomes (PLOs)

• PLO 1: Develop and implement advanced AI solutions using cutting-edge methodologies, algorithms, and technologies to address complex real-world problems across various domains. (ILO 2, ILO 4)
• PLO 2: Demonstrate proficiency in collaborative project management and effective communication of complex AI concepts to both technical and non-technical audiences. (ILO 1, ILO 5)
• PLO 3: Evaluate and optimize AI systems for performance, scalability, and security, particularly in cloud-based and distributed computing environments. (ILO 2, ILO 4)
• PLO 4: Synthesize ethical considerations, including those informed by a Christian worldview, in the development and deployment of AI solutions, demonstrating responsible AI practices and addressing issues of fairness, privacy, and societal impact. (ILO 3, ILO 5)
• PLO 5: Critically analyze current and emerging trends in AI, assessing their potential impact on business and society, and formulating innovative strategies for their application in organizational contexts. (ILO 2, ILO 4, ILO 5)