CV/ML algorithms are computational methods used in computer vision (CV) and machine learning (ML) to analyze visual data, recognize patterns, and make predictions. Computer vision focuses on enabling machines to interpret images or videos, while machine learning involves training models to learn from data. These algorithms often overlap—for example, a convolutional neural network (CNN) can be used for image classification (a CV task) but is also a core ML technique. The goal is to automate tasks that traditionally require human-like perception or decision-making, such as identifying objects in photos or predicting user behavior.
In computer vision, algorithms process pixel data to extract meaningful information. A common example is the CNN, which uses layers of filters to detect edges, textures, and complex features in images. For instance, a CNN trained on labeled photos can distinguish between cats and dogs by learning hierarchical patterns. Another CV algorithm is YOLO (You Only Look Once), which performs real-time object detection by dividing an image into grids and predicting bounding boxes and class probabilities. For segmentation tasks, U-Net is widely used in medical imaging to outline specific regions, like tumors in MRI scans. These algorithms rely on techniques like edge detection, feature matching, and geometric transformations to interpret visual data.
In machine learning, algorithms generalize from training data to make predictions or decisions. Supervised learning methods like support vector machines (SVMs) classify data by finding optimal boundaries between categories—for example, filtering spam emails based on keywords. Unsupervised algorithms like k-means clustering group unlabeled data into clusters, such as segmenting customers by purchase behavior. Reinforcement learning, used in robotics or game AI, trains agents to maximize rewards through trial and error. Practical applications include recommendation systems (collaborative filtering) and anomaly detection (isolation forests). Many ML algorithms, like decision trees or gradient-boosted models, are adaptable across domains, but their effectiveness depends on data quality, feature engineering, and hyperparameter tuning. CV/ML pipelines often combine these techniques—for example, using a CNN to extract image features, followed by a logistic regression model to classify them.