An Introduction to Machine Learning

Supervised Learning

Supervised learning is where an algorithm learns from labeled data. This means each input example has a corresponding correct output assigned to it. The goal is for the algorithm to learn a mapping function between inputs and outputs.

A classic example is training an algorithm to recognize images of cats versus dogs, where each image is explicitly labeled as either 'cat' or 'dog'. Common algorithms include linear regression (predicting continuous values) and support vector machines (classifying data).

f(x) = y  (Mapping input x to output y)

Unsupervised Learning

In unsupervised learning, the algorithm is given unlabeled data and tasked with finding patterns or structures on its own. There’s no ‘correct answer’ provided during training.

Popular techniques include clustering (grouping similar data points together) and dimensionality reduction (reducing the number of variables while preserving important information). K-means clustering is a common algorithm for this.

Cluster Similarity = Distance(x_i, x_j)

Reinforcement Learning

Reinforcement learning involves training an agent to make decisions in an environment to maximize a reward. The agent learns through trial and error, receiving positive or negative feedback for its actions.

Think of teaching a robot to walk – it receives a positive reward when it takes a step forward and a negative reward when it falls. Algorithms like Q-learning are frequently used.

Q(s,a) = R(s,a) + γ * max_a' Q(s', a') (Bellman Equation)

Model Evaluation

Once a machine learning model is trained, it’s crucial to evaluate its performance. This involves using metrics appropriate for the task – accuracy, precision, recall, F1-score for classification; mean squared error (MSE) or root mean squared error (RMSE) for regression.

Overfitting occurs when a model learns the training data too well and performs poorly on new, unseen data. Techniques like cross-validation are used to mitigate this.

Error = |Predicted Value - Actual Value|

Frequently asked questions

Try it live

Everything above runs in your browser — open SPH Fluid and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.