> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Robotics Weekly Study Guide

> Week-by-week study guide for the AI for Robotics course.

See the [Spring 2026 Academic Calendar](/calendar) for semester dates. Each week below lists the readings, lecture topics, and deliverables you should complete.

<AccordionGroup>
  <Accordion title="Week 1, Introduction">
    <Steps>
      <Step title="Review prerequisites">
        Python, linear algebra, probability theory, and basic control concepts. See [Prerequisites](/book/prerequisites/index).
      </Step>

      <Step title="Review lecture: Introduction to Robotics">
        AI and robotics from a systems perspective, with autonomous vehicles as the running example.
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [The Robotic AI Agent](https://www.youtube.com/watch?v=t9zxmEHGT1s), A practical map for navigating robotic AI systems.

        <Icon icon="video" /> [Development Environment Setup](https://www.youtube.com/watch?v=KCC2bgW-zaM), Setting up the ROS2 Docker-based development environment.

        <Icon icon="video" /> [Mathematical Prerequisites](https://www.youtube.com/watch?v=jkNugBJG2yk), Review the math foundations needed for the course.
      </Step>

      <Step title="Set up your development environment">
        Follow the [Dev Environment](/aiml-common/resources/environment/index) guide to install Docker and configure your container.
      </Step>

      <Step title="Import the course repository">
        [Import](https://docs.github.com/en/migrations/importing-source-code/using-github-importer/importing-a-repository-with-github-importer) [eng-ai-agents](https://github.com/pantelis/eng-ai-agents) to your GitHub account and clone it locally.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 2, Statistical Learning Theory">
    <Steps>
      <Step title="Read TIF Chapters 9 & 10">
        Introduction to learning and gradient-based learning algorithms from [Foundations of Computer Vision](https://visionbook.mit.edu/).
      </Step>

      <Step title="Read BISHOP Chapter 4">
        Single-variable and multivariate models, regularization, and Bayesian linear regression from [Deep Learning: Foundations and Concepts](https://www.bishopbook.com/).
      </Step>

      <Step title="Review lecture: Supervised Learning">
        Perception subsystem, reflexive agents, the learning problem. See [The Learning Problem](/aiml-common/lectures/learning-problem/index).
      </Step>

      <Step title="Review lecture: Linear Regression">
        Regression fundamentals and empirical risk minimization. See [Linear Regression](/aiml-common/lectures/regression/linear-regression/index).
      </Step>

      <Step title="Review lecture: SGD Optimization">
        Stochastic gradient descent for minimizing the empirical risk. See [SGD](/aiml-common/lectures/optimization/sgd/index).
      </Step>

      <Step title="Read GERON Chapter 4, SGD sections">
        Read the Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent, and Mini-Batch Gradient Descent sections from [Chapter 4: Training Linear Models](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch04.html).
      </Step>

      <Step title="Run the GERON Chapter 4 notebook">
        Work through the [Training Linear Models](https://github.com/ageron/handson-mlp/blob/main/04_training_linear_models.ipynb) notebook.
      </Step>

      <Step title="Run the SGD notebook">
        Execute the [SGD Sinusoidal Dataset](/aiml-common/lectures/regression/linear-regression/sgd_sinusoidal_dataset) notebook in your container.
      </Step>

      <Step title="Review lecture: Entropy">
        Information theory principles and cross-entropy. See [Entropy](/aiml-common/lectures/entropy/index).
      </Step>

      <Step title="Review lecture: Marginal Maximum Likelihood">
        Marginal likelihood and parameter estimation. See [Marginal Maximum Likelihood](/aiml-common/lectures/optimization/maximum-likelihood/marginal_maximum_likelihood).
      </Step>

      <Step title="Review lecture: Conditional Maximum Likelihood">
        Conditional likelihood for supervised learning. See [Conditional Maximum Likelihood](/aiml-common/lectures/optimization/maximum-likelihood/conditional_maximum_likelihood).
      </Step>

      <Step title="Review lecture: Classification Introduction">
        Classification fundamentals and decision boundaries. See [Classification Introduction](/aiml-common/lectures/classification/classification-intro/index).
      </Step>

      <Step title="Review lecture: Logistic Regression">
        Binary classification with logistic regression. See [Logistic Regression](/aiml-common/lectures/classification/logistic-regression/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [The Learning Problem](https://www.youtube.com/watch?v=Qw0Bdsa_3Yg), The Vapnik block diagram.

        <Icon icon="video" /> [Linear Regression](https://www.youtube.com/watch?v=oLP7v-jCGxM), Extracting non-linear patterns with linear models.

        <Icon icon="video" /> [Gradient Descent](https://www.youtube.com/watch?v=eWw5NPSxaa8), Optimizing complicated functions with iterative methods.

        <Icon icon="video" /> [Entropy](https://www.youtube.com/watch?v=V3FoNyppPl8), Information theory principles.

        <Icon icon="video" /> [Maximum Likelihood Estimation](https://www.youtube.com/watch?v=CYxadbxMZno), The workhorse of statistical modeling.

        <Icon icon="video" /> [Binary Classification](https://www.youtube.com/watch?v=eQ6UE968Xe4), Binary classification and Logistic Regression.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 3, Dense and Convolutional Neural Networks">
    <Steps>
      <Step title="Read DL Chapters 9 & 10">
        Convolutional Neural Network architecture and applications.
      </Step>

      <Step title="Review lecture: CNN Introduction">
        Convolution operations, pooling, and spatial feature hierarchies. See [CNN Introduction](/aiml-common/lectures/cnn/cnn-intro/index).
      </Step>

      <Step title="Review lecture: CNN Layers">
        Layer types and architectural patterns. See [CNN Layers](/aiml-common/lectures/cnn/cnn-layers/index).
      </Step>

      <Step title="Review lecture: CNN Architectures and ResNets">
        ResNet, VGG, and other architectures. See [CNN Example Architectures](/aiml-common/lectures/cnn/cnn-example-architectures/index) and [Feature Extraction with ResNet](/aiml-common/lectures/scene-understanding/feature-extraction-resnet/index).
      </Step>

      <Step title="Read GERON Chapter 12, CNN sections">
        Read the Convolutional Layers, Pooling Layers, and CNN Architectures sections from [Chapter 12: Deep Computer Vision with CNNs](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch12.html).
      </Step>

      <Step title="Run the GERON Chapter 12 notebook">
        Work through the [Deep Computer Vision with CNNs](https://github.com/ageron/handson-mlp/blob/main/12_deep_computer_vision_with_cnns.ipynb) notebook.
      </Step>

      <Step title="Submit Assignment 1">
        Complete and submit [Assignment 1](/aiml-common/assignments/main/robotics-spring-2026/assignment-1).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Feature Extraction](https://www.youtube.com/watch?v=M-bIqxvF984), Using a simple network to understand how features are extracted.

        <Icon icon="video" /> [Backpropagation](https://www.youtube.com/watch?v=IjK9R6r3mqk), How to calculate gradients in a neural network.

        <Icon icon="video" /> [Convolution and Correlation](https://www.youtube.com/watch?v=WXoOohWU28Y), A linear operation for extracting spatial features.

        <Icon icon="video" /> [CNN Architectures](https://www.youtube.com/watch?v=TV-DjM8242s), Looking inside a CNN layer and understanding architectural patterns.

        <Icon icon="video" /> [ResNets](https://www.youtube.com/watch?v=FCQ-rih6cHY), Residual Networks and skip connections.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 4, Object Detection">
    <Steps>
      <Step title="Review lecture: Detection Metrics">
        Evaluation metrics for object detection. See [Detection Metrics](/aiml-common/lectures/scene-understanding/object-detection/detection-metrics/index).
      </Step>

      <Step title="Review lecture: Object Detection">
        Detection pipelines and architectures. See [Object Detection Introduction](/aiml-common/lectures/scene-understanding/object-detection/object-detection-intro/index).
      </Step>

      <Step title="Review lecture: R-CNN">
        Region-based convolutional neural networks. See [R-CNN](/aiml-common/lectures/scene-understanding/object-detection/rcnn/index).
      </Step>

      <Step title="Review lecture: Fast R-CNN">
        Efficient region-based detection. See [Fast R-CNN](/aiml-common/lectures/scene-understanding/object-detection/fast-rcnn/index).
      </Step>

      <Step title="Review lecture: Faster R-CNN">
        Region proposal networks and two-stage detection. See [Faster R-CNN](/aiml-common/lectures/scene-understanding/object-detection/faster-rcnn/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Introduction to Object Detection](https://www.youtube.com/watch?v=ojb-E4tDTxs), Object detection in a physical security application.

        <Icon icon="video" /> [Computer Vision Datasets](https://www.youtube.com/watch?v=YNBPJx0EUcc), What types of annotations are used in computer vision?

        <Icon icon="video" /> [Region-based Object Detectors](https://www.youtube.com/watch?v=4WHFcqq1ErA), R-CNN, Fast R-CNN, Faster R-CNN.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 5, State Estimation">
    <Steps>
      <Step title="Read THRUN Chapters 2-3">
        Recursive estimation and Dynamic Bayesian Networks.
      </Step>

      <Step title="Review lecture: Recursive State Estimation">
        Bayes filter and its properties. See [Recursive State Estimation](/aiml-common/lectures/rse/recursive-state-estimation/index).
      </Step>

      <Step title="Review lecture: Kalman Filters">
        Linear Gaussian models and the Kalman update equations. See [Kalman Filters](/aiml-common/lectures/rse/kalman-filters/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Introduction to State Estimation with HMM](https://www.youtube.com/watch?v=qegibGSstNE), Introducing Hidden Markov Models.

        <Icon icon="video" /> [The Bayes Filter](https://www.youtube.com/watch?v=eiGC3e78JVw), Implementing the Bayes filter algorithm.

        <Icon icon="video" /> [Discrete Bayes Filter Example](https://www.youtube.com/watch?v=-Ub6AD3iTXE), Discrete Bayes localization notebook.

        <Icon icon="video" /> [Continuous State Space and Kalman Filter](https://www.youtube.com/watch?v=pAKSArqZE08), Localizing a drone under Gaussian assumptions.

        <Icon icon="video" /> [A Kalman Filter Example](https://www.youtube.com/watch?v=A5_WcG1s5Mc), Kalman localization notebook.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 6, Midterm Exam">
    <Steps>
      <Step title="Midterm exam">
        The midterm covers weeks 1-5. See the [Temporal Cycle-Consistency Learning exam notebook](/aiml-common/assignments/topics/self-supervised/temporal-cycle-consistency-learning/temporal-cycle-consistency-learning).
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 7, SLAM">
    <Steps>
      <Step title="Read THRUN Chapters 9-10; CORKE Chapter 6">
        Simultaneous Localization and Mapping, Visual SLAM with monocular cameras.
      </Step>

      <Step title="Review lecture: SLAM">
        The SLAM problem and solution approaches. See [SLAM](/aiml-common/lectures/rse/slam/index).
      </Step>

      <Step title="Read the SLAM lecture page and watch videos">
        Read the full [SLAM](/aiml-common/lectures/rse/slam/index) page and watch the three embedded videos:

        <Icon icon="video" /> [SLAM Overview](https://www.youtube.com/watch?v=0I30M6yTklo)

        <Icon icon="video" /> [Extended Kalman Filter (Cyrill Stachniss)](https://www.youtube.com/watch?v=DE6Jn2cB4J4)

        <Icon icon="video" /> [EKF SLAM (Cyrill Stachniss)](https://www.youtube.com/watch?v=XeWG5D71gC0)
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 8, MDPs">
    <Steps>
      <Step title="Review lecture: MDP Introduction">
        Sequential decisions, reward signals, and Bellman equations. See [MDP Introduction](/aiml-common/lectures/mdp/mdp-intro/index).
      </Step>

      <Step title="Review lecture: Bellman Equations">
        Expectation and optimality backups. See [Bellman Expectation](/aiml-common/lectures/mdp/bellman-expectation-backup/index).
      </Step>

      <Step title="Review lecture: Policy Improvement">
        From value functions to optimal policies. See [Policy Improvement](/aiml-common/lectures/mdp/policy-improvement/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Introduction to MDPs - Part 1](https://www.youtube.com/watch?v=15d1-FZJKP4), Defining Markov Decision Processes.

        <Icon icon="video" /> [Introduction to MDPs - Part 2](https://www.youtube.com/watch?v=_EZ78z5ryo0), Defining Markov Decision Processes.

        <Icon icon="video" /> [Bellman Expectation Equations - Part 1](https://www.youtube.com/watch?v=e2fz3nNjNcQ), Deriving the Bellman Expectation Equations.

        <Icon icon="video" /> [Bellman Expectation Equations - Part 2](https://www.youtube.com/watch?v=M8Gadmn8pA8), Deriving the Bellman Expectation Equations.

        <Icon icon="video" /> [Bellman Optimal Value Functions](https://www.youtube.com/watch?v=C4sloVtHSwo), Deriving the Bellman Optimality Equations.

        <Icon icon="video" /> [Policy Iteration and Value Iteration](https://www.youtube.com/watch?v=SSYDYSmGi1E), Using the Bellman Optimality Equations for optimal control.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 9, Deep Reinforcement Learning">
    <Steps>
      <Step title="Review lecture: Reinforcement Learning">
        Model-free methods for robotic control policies. See [Reinforcement Learning](/aiml-common/lectures/reinforcement-learning/index).
      </Step>

      <Step title="Review lecture: Temporal Difference Learning">
        TD methods and SARSA. See [Temporal Difference](/aiml-common/lectures/reinforcement-learning/value-based-algorithms/prediction/temporal-difference).
      </Step>

      <Step title="Run the SARSA Gridworld notebook">
        Execute the [SARSA Gridworld](/aiml-common/lectures/reinforcement-learning/value-based-algorithms/control/sarsa/gridworld/sarsa_gridworld) notebook.
      </Step>

      <Step title="Run the GERON Chapter 19 notebook">
        Work through the [Reinforcement Learning](https://github.com/ageron/handson-mlp/blob/main/19_reinforcement_learning.ipynb) notebook.
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Monte Carlo Prediction](https://www.youtube.com/watch?v=kVYyDO0B6xo), Estimating value functions from sampled episodes.

        <Icon icon="video" /> [Temporal Difference Learning](https://www.youtube.com/watch?v=L90h-1Sntnw), Bootstrapping value estimates from one-step transitions.

        <Icon icon="video" /> [Model-free Control](https://www.youtube.com/watch?v=gkflVEhnA5s), SARSA, Q-learning, and learning optimal policies without a model.

        <Icon icon="video" /> [Proximal Policy Optimization - Part 1](https://www.youtube.com/watch?v=yOStXTxK1aw), PPO foundations and clipped surrogate objective.

        <Icon icon="video" /> [Proximal Policy Optimization - Part 2](https://www.youtube.com/watch?v=V36g0nEgOyY), PPO implementation details and practical considerations.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 10, Instruction Following">
    <Steps>
      <Step title="Review lecture: Vision-Language Models">
        VLMs for human-robot collaboration. See [VLM Introduction](/aiml-common/lectures/vlm/index).
      </Step>

      <Step title="Review lecture: Imitation Learning">
        Learning from demonstrations for robotic manipulation. See [Imitation Learning](/aiml-common/lectures/imitation-learning/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Introduction to Transformers](https://www.youtube.com/watch?v=2pIIPXPopzc), The transformer architecture and the simple attention mechanism.

        <Icon icon="video" /> [The Learnable Attention Mechanism](https://www.youtube.com/watch?v=JdoXFcSiDrA), Implementing the scaled dot-product self attention mechanism.

        <Icon icon="video" /> [Multi-Head Self Attention](https://www.youtube.com/watch?v=hKCO8PO3yvw), Using multiple attention heads to capture different aspects of input sequences.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 11, VLA Models">
    <Steps>
      <Step title="Review lecture: VLA Agents">
        End-to-end Vision-Language-Action models for instruction parsing, perception, and action. See [VLA Agents](/book/vla-agents/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> Coming soon, VLA models video lectures are in development.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 12, Global Planning">
    <Steps>
      <Step title="Review lecture: Search Algorithms">
        Optimal planning under uncertainty, A\*, D\*, RRT\*, PRM algorithms. See [Search](/aiml-common/lectures/planning/global-planning/search/index).
      </Step>

      <Step title="Review lecture: A* Algorithm">
        Heuristic search for path planning. See [A\*](/aiml-common/lectures/planning/global-planning/search/a-star/index).
      </Step>

      <Step title="Watch videos">
        <Icon icon="video" /> [Introduction to Planning](https://www.youtube.com/watch?v=SgF46ifnESY), Typical planning problems and PDDL.

        <Icon icon="video" /> [Planning Domain Definition Language](https://www.youtube.com/watch?v=BauJR11F72E), The constructs of PDDL.

        <Icon icon="video" /> [Forward Search Algorithms](https://www.youtube.com/watch?v=LwAiOhW9VAk), Finding global planning solutions with forward search.

        <Icon icon="video" /> [The A\* Algorithm](https://www.youtube.com/watch?v=5gr4TTpVG5w), Using heuristics to guide forward search.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 13, Sim-to-Real Transfer">
    <Steps>
      <Step title="Review lecture: Sim-to-Real Transfer">
        Training in simulation and deploying to real robots. See [Sim-to-Real Transfer](/aiml-common/lectures/sim2real/index).
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

***

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