What this section covers
Machine learning is the engine beneath every modern RL algorithm. Before you can implement DQN, PPO, or any deep RL method, you need to be fluent in supervised learning concepts β loss functions, gradient descent, classification, and model evaluation. This section builds that foundation systematically, from first principles to scikit-learn.
Topics covered:
- What machine learning is and how it differs from traditional programming
- How data is structured as features and labels for ML models
- Linear regression: MSE loss, the gradient, and one gradient step
- Gradient descent: the optimization algorithm that trains every neural network
- Multiple regression: matrix form, NumPy vectorization, multi-feature problems
- Classification concepts: decision boundaries, sigmoid, binary decisions
- Logistic regression: cross-entropy loss, softmax policy connection
- Model evaluation: accuracy, precision, recall, F1, confusion matrices
- Overfitting and underfitting: regularization, train/test splits, biasβvariance
- Scikit-learn workflows: pipelines, model selection, cross-validation
- Decision trees and random forests: non-linear models and feature importance
- Neural network basics: layers, activations, forward pass
- Backpropagation: the algorithm that computes gradients in deep networks
- Review and bridge: how every concept here reappears inside RL algorithms
Why ML foundations matter for RL
RL IS ML. Understanding supervised learning first makes every RL algorithm click:
| ML concept | Where it reappears in RL |
|---|---|
| Linear regression | Value function approximation \(V(s) = w^T \phi(s)\) |
| Gradient descent | Policy gradient, Q-learning updates |
| Classification | Policy \(\pi(a \mid s)\) β choosing an action from a state |
| Logistic regression | Softmax policy over discrete actions |
| Cross-entropy loss | Policy gradient objective |
| Overfitting | Generalization in deep RL agents |
| Neural networks | Deep Q-Networks (DQN), actorβcritic networks |
| Backpropagation | How policy and value networks are trained |
Every page in this section ends with an explicit RL connection so you always know why you are learning it.
Table of contents
| # | Page | Topic |
|---|---|---|
| 1 | What is ML? | Three types of ML, supervised vs RL |
| 2 | Datasets and Features | X, y, DataFrames, pandas |
| 3 | Linear Regression | MSE, gradient, one step |
| 4 | Gradient Descent | Learning rate, loss curves |
| 5 | Multiple Regression | Matrix form, NumPy |
| 6 | Classification Concepts | Decision boundary, sigmoid |
| 7 | Logistic Regression | Cross-entropy, gradient update |
| 8 | Model Evaluation | Accuracy, precision, recall, F1 |
| 9 | Overfitting | Regularization, train/test split |
| 10 | Scikit-learn Workflows | Pipelines, cross-validation |
| 11 | Decision Trees | Non-linear models, feature importance |
| 12 | Neural Networks Intro | Layers, activations, forward pass |
| 13 | Backpropagation | Chain rule, gradient flow |
| 14 | Review and Bridge to RL | Connecting everything to RL |
Quick-start guide
- Complete pages in order. Each page builds on the previous one. Do not skip.
- Do every exercise. The pyrepl blocks run in your browser β no setup needed.
- Check the worked solutions only after a genuine attempt. The struggle is where the learning happens.
- Use the extra practice items. Items 5 (Debug) and 3 (Challenge) are especially valuable.
- Revisit the RL connection at the bottom of each page. Ask yourself: “Where have I seen this in RL already?”
Estimated time: 2β4 hours per page for a thorough reading + all exercises. The full section takes approximately 35β50 hours.
Assessment checkpoints
After every four pages, check your understanding:
- After page 4 β Checkpoint A: Regression and Optimization β Can you implement gradient descent from scratch in NumPy?
- After page 7 β Checkpoint B: Classification β Can you train logistic regression and explain cross-entropy?
- After page 10 β Checkpoint C: Evaluation and sklearn β Can you evaluate a model correctly and avoid overfitting?
- After page 14 β Checkpoint D: Bridge to RL β Can you name the RL equivalent of each ML concept?