A series of notebooks covering key topics in machine learning along with a focus on the maths underpinning these methods

Optimization is a core component of machine learning that involves finding the best parameters for a model to minimize or maximize a certain objective function. Machine learning algorithms often involve optimization to adjust model parameters during training. Techniques like gradient descent and its variants are widely used to iteratively update parameters to achieve better model performance.

Support Vector Machines (SVMs) are powerful algorithms for classification and regression tasks. SVMs aim to find a hyperplane that best separates different classes, maximizing the margin between them. They work particularly well in scenarios with high-dimensional data and can handle both linear and non-linear separation by using kernels. SVMs are used in various applications, from image classification to text analysis.

Neural Networks are the foundation of deep learning, a subfield of machine learning that has achieved remarkable success in various domains. Neural networks are composed of interconnected layers of nodes, mimicking the structure of the human brain. They can learn complex patterns from data, making them suitable for tasks like image recognition, natural language processing, and reinforcement learning.

Gaussian Processes provide a probabilistic framework for modeling functions. They offer a way to capture uncertainty in predictions and can be used for regression, classification, and optimization tasks. Gaussian Processes are particularly valuable when data is limited or noisy, allowing for principled uncertainty estimation. They find applications in tasks such as surrogate modeling, hyperparameter tuning, and Bayesian optimization.

Convolutional Neural Networks (CNNs) are a specialized type of neural network designed for processing grid-like data, such as images. They use convolutional layers to automatically learn hierarchical features from raw pixel values. CNNs have revolutionized computer vision tasks, achieving state-of-the-art results in image classification, object detection, and image segmentation.

Generative Modeling involves creating models that can generate new data samples similar to a given dataset. Models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) fall under this category. VAEs learn to encode and decode data while GANs use a generator and discriminator to create realistic samples. Generative models have applications in image synthesis, data augmentation, and anomaly detection.