You can download the lectures here. I will try to upload lecture slides within a few days to a week after the day of the lecture.

  • January 26, 2026
    tl;dr: Introduction to Senior Project I
    [slides]

    Introduction to Senior Project I.

  • January 28, 2026
    tl;dr: Overview of Class Expectations
    [slides]

    Overview of course expectations, deliverables, and grading criteria.

  • February 2, 2026
    tl;dr: Research Methods and Basic AI Evaluation
    [slides]

    Suggested Readings:

    Overview of research methods, how to read research papers, and an introduction to AI evaluation metrics (Precision, Recall, F-Measure).

  • February 4, 2026
    tl;dr: Single vs. Multi-Agent AI and Complexity
    [slides]

    Introduction to Single-Agent vs. Multi-Agent systems, GridWorld examples, Markov Decision Processes (MDP), and the curse of dimensionality in multi-agent environments.

  • February 9, 2026
    tl;dr: Generalizability and Reward Modeling
    [slides]

    Overview of Generalizability in AI Agents versus simple memorization. The lecture covers Reward Modeling strategies, including the trade-offs between Dense (hackable) and Sparse (slow) rewards, as well as advanced techniques like Curriculum Learning and Inverse Reinforcement Learning (IRL).

  • February 11, 2026
    tl;dr: Distributed Processing for AI Agents: Modalities (tabular, graphical, multi-modal)
    [slides]

    Overview of the hierarchy of decision-making models, moving from simple MDPs to Partially Observable Stochastic Games (POSG). The lecture also explores the necessity of distributed processing for AI agents, covering Data versus Model Parallelism and the differences between Synchronous and Asynchronous execution.

  • February 18, 2026
    tl;dr: Systems: Latency and Bandwidth balancing
    [slides]

    Exploration of the physical limits of AI agents regarding latency, bandwidth, and throughput. The lecture contrasts Cloud versus Edge architectures and discusses engineering solutions for system constraints, such as model compression, quantization, and activation offloading, particularly in latency-critical scenarios like autonomous braking.

  • February 23, 2026
    tl;dr: Deep Reinforcement Learning (RL) & Multi-Agent Deep Reinforcement Learning (MARL): Training/Finetuning AI Models, Transformers, Q Learning, Deep Q Networks and the Bellman Equation
    [slides]

    The Transformer architecture and its applications in Deep Reinforcement Learning (RL) and Multi-Agent Deep Reinforcement Learning (MARL). The lecture covers training and finetuning AI models, the Bellman Equation, Q Learning, and Deep Q Networks, providing insights into their roles in decision-making processes.

  • February 25, 2026
    tl;dr: Reinforcement Learning (RL) & Multi-Agent Deep Reinforcement Learning (MARL): REINFORCE, Independent Q Learning and Convergence
    [slides]

    Walkthroughs on DQL, Experience Buffers, and comparison of Q-Learning with REINFORCE.

  • March 2, 2026
    tl;dr: Sample Complexity, Generalizability, IQL, Actor-Critic, MADDPG, and Imitation Learning
    [slides]

    Discussion on Sample Complexity, Generalizability, Independent Q Learning (IQL), Actor-Critic methods, Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and Imitation Learning methods (ehavioral Cloning, DAgger) in the context of Reinforcement Learning (RL) and Multi-Agent Deep Reinforcement Learning (MARL).

  • March 4, 2026
    tl;dr: Decision Making Algorithms for AI Agents like AlphaGo (RL + MCTS), TRPO, PPO, DPO, GRPO, StrateGo, and Intelligent AI Delegation
    [slides]

    Decision Making Algorithms for AI Agents like AlphaGo (RL + MCTS), TRPO, PPO, DPO, GRPO, StrateGo, and Intelligent AI Delegation

  • March 9, 2026
    tl;dr: Robustness, Consistency and Risk Mitigation in AI Agents & Offline Reinforcement Learning and Inverse Reinforcement Learning
    [slides]

    Discussion on Robustness, Consistency and Risk Mitigation in AI Agents, Offline Reinforcement Learning, and Inverse Reinforcement Learning (IRL) in the context of Reinforcement Learning (RL) and Multi-Agent Deep Reinforcement Learning (MARL).

  • March 11, 2026
    tl;dr: Model Predictive Control (MPC): Real-time constraints, mistake correction and daily control tasks by AI Agents
    [slides]

    Discussion on Model Predictive Control (MPC): Real-time constraints, mistake correction and daily control tasks by AI Agents in the context of Reinforcement Learning (RL) and Multi-Agent Deep Reinforcement Learning (MARL).

  • March 16, 2026
    tl;dr: Game Theory for AI Agents
    [slides]

    Discussion on Game Theory for AI Agents in the context of Reinforcement Learning (RL) and Multi-Agent Deep Reinforcement Learning (MARL).