CS 2200: Introduction to AI and Applications

CS 2200: Introduction to AI and Applications

Semester Hours:   3.0
Contact Hours:    3
Coordinator:   TBD
Text:   TBD
Author(s):   TBD
Year:   TBD

SPECIFIC COURSE INFORMATION

Catalog Description

This course provides a comprehensive, high level introduction to Artificial Intelligence (AI) and Machine Learning (ML), focusing on its core concepts and practical applications. Students will gain an understanding of AI/ML, deep learning, and generative AI, while developing hands-on skills in applying AI/ML models. The course also emphasizes AI/ML applications across various disciplines. Approved for distance learning. Prerequisites: CS 1010.

Course type: REQUIRED

SPECIFIC COURSE GOALS

• Explain foundational concepts of AI/ML (e.g., machine learning, deep learning, and generative AI)
• Apply data analysis and preprocessing techniques to prepare datasets for AI/ML models
• Apply AI/ML models using appropriate libraries, frameworks, and tools and interpret model results
• Apply AI/ML models or tools for problem-solving in different application domains
• Explain the key stages involved in the development of an AI/ML project

LIST OF TOPICS COVERED

• Introduction to AI (0.5 weeks)
  • Defining AI, ML, DL, and Generative AI, their history, and real-world applications
  • Types of AI (narrow, general, super)
• Data Basics (1.5 weeks)
  • Data types and formats
  • Data visualization basis and implementation
  • Data creation overview
• Introduction to Machine Learning (2.5 weeks)
  • Supervised vs. unsupervised learning
  • Common ML tasks (classification, regression, clustering)
• Deep Learning Overview (2 weeks)
  • Neural networks: common architectures and optimization overviews
  • Implementation of classical ANNs with python
• Generative AI Fundamentals (2 weeks)
  • Concepts of generative models
  • Applications of classical generative AI: data generation with GANs and VAEs
• AI in Business and Economics (1 week)
  • Predictive analytics, fraud detection, customer segmentation, financial modeling
• AI in Healthcare (1 week)
  • Medical imaging analysis, disease prediction, drug discovery, patient monitoring
• AI in Social Sciences and Humanities (1 week)
  • Natural language processing applications (e.g., sentiment analysis, text summarization), social network analysis, digital humanities
• AI in Other Fields (1 week)
  • Examples from fields like education, agriculture, environment, and more
• AI Development Lifecycle (1 week)
  • Key stages in AI project development
  • Project management and collaboration
• Ethical Considerations in AI (0.5 week)
  • Bias in AI/ML, fairness, accountability, transparency, privacy principles