Unsupervised machine learning techniques and their applications. Unsupervised learning involves training models on unlabeled data to find patterns or structures within the data. Here are a few examples:

1. Clustering: Clustering is a common unsupervised technique that groups similar data points together based on certain features. One popular algorithm for clustering is K-Means. Applications include customer segmentation, image compression, and anomaly detection.
2. Dimensionality Reduction: Dimensionality reduction techniques aim to reduce the number of features in a dataset while preserving important information. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are commonly used for visualization and feature selection.
3. Anomaly Detection: Anomaly detection identifies data points that deviate significantly from the norm. It’s used in fraud detection, network security, and manufacturing quality control. Autoencoders and One-Class SVM are commonly used algorithms for this purpose.
4. Topic Modeling: Topic modeling extracts underlying themes from text data. Latent Dirichlet Allocation (LDA) is a widely used algorithm for topic modeling. It’s used in applications like content recommendation and sentiment analysis.
5. Density Estimation: Density estimation techniques model the probability distribution of data points in a dataset. Gaussian Mixture Models (GMM) and Kernel Density Estimation (KDE) are examples. These techniques are used in anomaly detection and image segmentation.
6. Collaborative Filtering: Collaborative filtering is used in recommendation systems to suggest items based on user preferences and behavior. Matrix Factorization and Alternating Least Squares (ALS) are common algorithms for collaborative filtering.
7. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, that work together to generate new data samples that resemble a given dataset. GANs are used for image synthesis, style transfer, and data augmentation.
8. Association Rule Mining: Association rule mining discovers relationships between items in a dataset. Apriori and FP-Growth are algorithms used for this purpose. Applications include market basket analysis and recommendation systems.

When sending bulk emails with course information, you can leverage unsupervised learning to segment your email list based on user behavior or preferences. This could improve the targeting of your emails and reduce the likelihood of them being marked as spam. However, it’s important to note that ensuring emails don’t go to spam also depends on factors such as email content, sender reputation, and recipient engagement.