Course Description

We are entering an age in which data and AI are deeply shaping science, technology, business, government, education, and daily life. Massive amounts of data come from sources like the internet, social media, mobile phones, cameras, sensors, transactions, scientific instruments, and increasingly, AI systems themselves. This “information burst” has enabled modern digital services and laid the foundation for machine and deep learning. Recently, foundation models such as GPT have shown how large-scale data, powerful neural networks, and massive computing can produce systems capable of language understanding, reasoning, coding, multimodal generation, and tool use. However, this progress also raises challenges: data quality, privacy leakage, security risks, bias, hallucination, copyright issues, and over-trust of AI outputs.

This course aims to help students understand the full pipeline from data to intelligence to real-world applications. Students will learn how data is generated, digitized, stored, organized, cleaned, visualized, and analyzed, as well as the fundamentals of machine learning and deep learning—including supervised, unsupervised, and reinforcement learning, neural networks, and architectures like Transformers. The course will also introduce large language models, prompting, reasoning, AI agents, coding agents, and AI-assisted workflows. Throughout, students will explore how Big Data and AI are used across domains, how they affect society, and how to address risks related to privacy, security, credibility, fairness, and responsible use. By the end, students are expected not only to understand Big Data and AI conceptually but also to develop the judgment to use AI tools effectively and responsibly in real-world contexts.

Course Learning Outcomes

On completing the course, students will be able to:

1. Describe and explain why and how Big Data impacts different aspects of the society.
2. Analyze and understand the effect of Big Data on social and moral values.
3. Apply the understanding of security issues of Big Data to the protection of personal data, or new kinds of data appearing in the future.
4. Describe and explain the conditions under which a given piece of data can be trusted.
5. Apply the knowledge about the trust on Big Data to improve the quality of confidence in a given piece of data.
6. Describe and understand the basic principles of organizing and searching Big Data.
7. Apply data organizing and searching methodologies to organize a potentially large amount of personal information.

Offer Semester and Day of Teaching

Second semester (Wed)