Schedule

Introduction to the course, its goals, and the foundational concepts of distributed systems and data mining. This lecture sets the stage for understanding how large-scale data processing frameworks can be used in conjunction with AI agents to perform complex tasks efficiently.

This class covers frequent-itemset mining, pattern extraction, and the statistical properties that determine whether data is reliable and useful in practice.

Students examine locality-sensitive hashing, semantic search, and deduplication strategies for agent memory and retrieval pipelines.

We focus on DGIM, sketches, and stream-monitoring concepts for robotic, sensor, and energy-aware systems operating in real time.

This lecture introduces PageRank, HITS, and citation-network mining as tools for understanding complex research and information networks.

Students explore clustering for large data and the role of proximity-based methods in vision and multimodal problem settings.

This lecture connects game theory, coordination, and practical control to the design of robust data-driven multi-agent systems.

The lecture covers evolutionary and self-play learning approaches to continual adaptation in real-time environments.

This session explores classification, prediction, embedding representations, and subspace discovery for high-dimensional data.

Students investigate graph mining, network analysis, and the role of GNNs and graph benchmarks in modern data-mining workflows.

This lecture addresses data requirements, optimization-based decision making, and forecasting models such as TimesFM.

The class covers item response theory, text ranking, and literature-based discovery methods for hypothesis generation.

This session examines AI co-scientist architectures, agent debate, and evaluation frameworks such as Elo-based tournament evolution.

We examine knowledge-graph reasoning, graph retrieval augmentation, and hierarchical community detection for robust inference.

This session focuses on epistemic honesty, replication concerns, ghost evidence, and rigorous evaluation in agent-driven research systems.

The final lecture explores coordination, competition, and control patterns across human-agent and agent-agent systems.

Students present their final projects, showing how they applied data-mining, agentic design, evaluation, and research reasoning throughout the semester.