DATASCI 185 - Introduction to AI Applications

Welcome to DATASCI 185! This course provides a comprehensive introduction to the fundamental concepts, tools, and techniques used in artificial intelligence. The course is practical: students will learn how modern systems are designed and how to ask the right questions about data, reliability and harms. It combines short demonstrations (no programming experience required), case studies, and project work. It is suitable for undergraduate students from any faculty who want a grounded understanding of what AI can and cannot do.

We will meet every Monday and Wednesday from 4pm to 4:50pm in the Psychology Building, Room 290. It is important to read the assigned readings before class, and make sure to check this website for updates and additional resources. If you have any questions, please feel free to contact me or the course TAs. I am here to help you succeed in this course and beyond.

Course Content

The course covers six main modules:

  1. Orientation - Introduction to AI and course overview
  2. How AI systems are designed - Dataset design, learning paradigms
  3. Language and perception - Natural language processing and computer vision
  4. Retrieval, generation and pipelines - Modern AI systems and workflows
  5. Data ethics and bias - Ethical considerations and bias mitigation
  6. Policy, governance and social impact - Regulation, privacy, and societal effects

Contact Information

Learning Outcomes

By the end of this course, students will be able to:

  1. Explain the main ideas behind contemporary AI systems in plain language.
  2. Identify common failure modes of AI systems and the data issues that cause them.
  3. Read and assess claims about AI in news articles, product pages and policy documents.
  4. Design a small, realistic plan for an AI application, including data needs, evaluation, and a basic harm-mitigation strategy.
  5. Reflect critically on ethical, legal and social questions raised by AI deployment.

Website Structure

This website contains the course syllabus, lecture materials, and assignments for the course. The course repository at https://github.com/danilofreire/datasci185 is similarly structured. Feel free to explore the materials and use them as needed.

Getting Help

If you encounter any issues with the course materials or have questions about the content, please:

  1. Check the course syllabus and this README for relevant information
  2. Review the lecture materials in the repository
  3. Contact the TAs or instructor via email
  4. Open an issue on GitHub

Contributing to the Repository

While this repository is primarily maintained by the course instructor, everyone is welcome to contribute. Please feel free to suggest improvements or report issues by opening a GitHub issue, submitting a pull request, creating a discussion post, or contacting the instructor directly.

License

This repository is licensed under the MIT License. You are free to use, modify, and distribute the materials as needed, with appropriate attribution to the original source.

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