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 data and model parallelism, multimodal data, and the MapReduce framework before exploring data quality, association rules, and frequent-itemset mining with Apriori and FP-Growth.

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.

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.