nikbearbrown/csye_info_7374_research_methods_in_ai

CSYE_INFO 7374 Research Methods in AI

CSYE/INFO 7374 Research Methods in AI

Course Description

Welcome to the world of Research Methods in Artificial Intelligence, a comprehensive exploration of the methodologies and techniques essential for conducting effective research in the field of AI. In this course, you will embark on a journey that will equip you with the knowledge and skills needed to navigate the complex landscape of AI research.

Course Highlights

• AI Research Foundations: Dive deep into the foundational principles of AI research, including problem formulation, hypothesis development, and data collection.
• Advanced Data Analysis: Explore cutting-edge data analysis techniques specific to AI research, such as machine learning algorithms and statistical analysis.
• Ethical AI: Discuss the ethical considerations surrounding AI research, including responsible AI usage and implications for society.
• Practical Applications: Apply your newfound knowledge to real-world AI problems and projects, gaining hands-on experience in AI research.
• Expert Instruction: Learn from experienced AI researchers and practitioners who will guide you through the intricacies of conducting research in this dynamic field.
• Collaborative Learning: Engage in collaborative discussions and projects with fellow students, fostering a community of AI researchers.

By the end of this course, you will have a solid foundation in AI research methods and data analysis, enabling you to contribute effectively to the advancement of AI technology and its applications. Join us on this exciting journey into the world of AI research and innovation.

This course introduces students to the fundamental research methods, data analysis techniques, and reporting strategies necessary to conduct meaningful inquiry and research in the field of Artificial Intelligence (AI). Students will gain insight into research intent and design, methodology and techniques, format and presentation, and data management and analysis, all informed by commonly used statistical and AI methods. The course aims to equip students with the skills and knowledge required to contribute effectively to AI research and development.

Learning Objectives

Upon successful completion of this course, students should be able to:

1. Formulate research hypotheses and define research problems in AI.
2. Select appropriate research methodologies for AI investigations.
3. Collect, process, and analyze data relevant to AI research.
4. Effectively present research findings and data analysis to various stakeholders.
5. Evaluate and critique AI research reports and proposals.
6. Apply research findings to real-world AI applications and decision-making.

The course expectation is that a paper and project will be published by the end of the term.

Weekly Schedule

• Introduction and Basic Research Concepts (2 weeks)

  • Research in the field of AI
  • Ethical considerations in AI research
  • Identifying research hypotheses and questions
  • Reviewing relevant AI literature
  • Introduction to data collection and analysis in AI

• Qualitative Research Methods in AI (2 weeks)

  • Understanding qualitative data in AI
  • Qualitative data collection techniques
  • Qualitative data analysis procedures
  • Coding and thematic development in AI research

• Quantitative Research Methods and Statistics in AI (6 weeks)

  • Types of quantitative data in AI
  • Sampling concepts in AI research
  • Quantitative data collection instruments
  • Introduction to applied statistics in AI
  • Descriptive and inferential statistics in AI
  • Regression analysis, t-tests, ANOVA, correlations, and chi-square in AI research

• Mixed Methods Research in AI (1 week)

  • Advantages and design components of mixed methods research in AI
  • Explanatory and exploratory mixed methods frameworks in AI research

• Reporting Results of Data Analysis in AI (3 weeks)

  • Presenting quantitative and qualitative findings in AI research reports
  • Writing about AI research findings
  • Critically reviewing and critiquing AI research reports

• Completing the AI Research Project (2 weeks)

  • Applying research findings to AI applications
  • Finalizing and presenting the AI research project