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

# AI Weekly Study Guide

> Week-by-week study guide for the Introduction to Artificial Intelligence course.

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

<AccordionGroup>
  <Accordion title="Week 1, Introduction to AI">
    <Steps>
      <Step title="Read AIMA Chapters 1 & 2">
        Systems approach to AI. Intelligent agents and their environments.
      </Step>

      <Step title="Review lecture: Introduction to AI">
        Overview of AI agents, course roadmap, and the systems approach. See [lecture notes](/aiml-common/lectures/introductions/ai-course-introduction/index).
      </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" /> [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>

      <Step title="Clone the Hands-On ML repository">
        Clone the companion repository for [Hands-On Machine Learning with Scikit-Learn and PyTorch](https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/) by Aurélien Géron: [github.com/ageron/handson-mlp](https://github.com/ageron/handson-mlp). This repository contains all the notebook exercises referenced throughout the course.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 2, Statistical Learning Theory I">
    <Steps>
      <Step title="Read AIMA Chapter 19">
        Learning from examples: classification and regression with classical ML.
      </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="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.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 3, Statistical Learning Theory II">
    <Steps>
      <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="Submit Assignment 1">
        Complete and submit [Assignment 1](/aiml-common/assignments/main/ai-spring-2026/assignment-1).
      </Step>

      <Step title="Watch videos">
        <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 4, Deep Neural Networks">
    <Steps>
      <Step title="Read AIMA Chapter 21 and DL Chapter 6">
        From Perceptron to MLPs, backpropagation fundamentals.
      </Step>

      <Step title="Review lecture: DNN Introduction">
        Neural network architectures and forward pass. See [DNN Introduction](/aiml-common/lectures/dnn/dnn-intro/index).
      </Step>

      <Step title="Review lecture: Backpropagation">
        Gradient computation and the chain rule. See [Backpropagation](/aiml-common/lectures/dnn/backprop-intro/index).
      </Step>

      <Step title="Read GERON Chapter 9, Classification MLPs">
        Read the Classification MLPs section from [Chapter 9: Artificial Neural Networks](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch09.html).
      </Step>

      <Step title="Run the GERON Chapter 9 notebook">
        Work through the [Artificial Neural Networks](https://github.com/ageron/handson-mlp/blob/main/09_artificial_neural_networks.ipynb) notebook.
      </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" /> [Multiclass Classifier](https://www.youtube.com/watch?v=JW22NeQXk64), A simple multiclass classifier example.

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

        <Icon icon="video" /> [Regularization](https://www.youtube.com/watch?v=a-ePQQZgA0E), How to regulate the complexity of a neural network.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 5, Convolutional Neural Networks">
    <Steps>
      <Step title="Read DL Chapters 9 & 10, AIMA Chapter 25">
        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="Watch videos">
        <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" /> [What CNNs Learn](https://www.youtube.com/watch?v=lb_5AdUpfuA), Visualizing the features learned by CNNs.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Week 6, Scene Understanding">
    <Steps>
      <Step title="Read AIMA Chapter 25">
        Object detection, semantic and instance segmentation.
      </Step>

      <Step title="Review lecture: Scene Understanding">
        Detection and segmentation pipelines. See [Scene Understanding](/aiml-common/lectures/scene-understanding/scene-understanding-intro/index).
      </Step>

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

      <Step title="Watch videos">
        <Icon icon="video" /> [Introduction to Object Detection](https://www.youtube.com/watch?v=ojb-E4tDTxs), Fundamentals of object detection pipelines.

        <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 7, Probabilistic Reasoning Over Time">
    <Steps>
      <Step title="Read AIMA Chapters 12, 13 & 14">
        Probability, Bayesian networks, and recursive state estimation.
      </Step>

      <Step title="Review lecture: Recursive State Estimation">
        Dynamic Bayesian Networks and Kalman filters. 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. See [Kalman Filters](/aiml-common/lectures/rse/kalman-filters/index).
      </Step>

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

  <Accordion title="Week 8, NLP Fundamentals">
    <Steps>
      <Step title="Read AIMA Chapter 23">
        Natural language processing problem formulation and component mechanics.
      </Step>

      <Step title="Review lecture: NLP Pipelines">
        Tokenization, embeddings, and NLP pipeline components. See [NLP Pipelines](/aiml-common/lectures/nlp/nlp-introduction/nlp-pipelines/index).
      </Step>

      <Step title="Review lecture: Word2Vec">
        Word embeddings and distributional semantics. See [Word2Vec](/aiml-common/lectures/nlp/nlp-introduction/word2vec/index).
      </Step>

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

  <Accordion title="Week 9, Language Modeling">
    <Steps>
      <Step title="Read AIMA Chapter 24, DL Chapter 10">
        RNN/LSTM architectures, attention mechanisms, Transformer framework.
      </Step>

      <Step title="Review lecture: RNNs and LSTMs">
        Recurrent architectures for sequence modeling. See [RNN Introduction](/aiml-common/lectures/rnn/introduction/index).
      </Step>

      <Step title="Review lecture: Transformers">
        Self-attention and the Transformer architecture. See [Transformers Introduction](/aiml-common/lectures/nlp/transformers/transformers-intro).
      </Step>

      <Step title="Run the GERON Chapter 13 notebook">
        Work through the [Processing Sequences Using RNNs and CNNs](https://github.com/ageron/handson-mlp/blob/main/13_processing_sequences_using_rnns_and_cnns.ipynb) notebook.
      </Step>

      <Step title="Midterm preparation">
        Review all material from Weeks 1-8. Focus on key concepts: supervised learning, DNNs, CNNs, probabilistic models, and NLP fundamentals.
      </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 10, Knowledge Representation">
    <Steps>
      <Step title="Read AIMA Chapter 7">
        Symbolic AI, propositional logic, theorem proving, Knowledge Base construction.
      </Step>

      <Step title="Review lecture: Reasoning">
        Logical reasoning and knowledge representation. See [Logic](/book/logic/index).
      </Step>

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

  <Accordion title="Week 11, Problem Solving and Search">
    <Steps>
      <Step title="Read AIMA Chapters 3, 4 & 11">
        Planning, A\* algorithm, handling deterministic and stochastic environments.
      </Step>

      <Step title="Review lecture: Search Algorithms">
        Forward search and A\*. See [Search](/aiml-common/lectures/planning/global-planning/search/index).
      </Step>

      <Step title="Review lecture: Task Planning">
        PDDL and classical planning. See [Task Planning](/book/task-planning/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 12, MDPs and POMDPs">
    <Steps>
      <Step title="Read AIMA Chapters 16 & 17">
        Sequential decision making, reward signals, policy computation.
      </Step>

      <Step title="Review lecture: MDP Introduction">
        Markov Decision Processes and the 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="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" /> [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>
</AccordionGroup>

***

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