The correct answer is: A. Spam detection, Pattern detection, Natural Language Processing
Supervised learning is a type of machine learning in which the model is trained on a set of labeled data. The model learns to map the input data to the output labels. Supervised learning is used in a variety of applications, including spam detection, pattern detection, natural language processing, image classification, and real-time visual tracking.
Spam detection is the process of identifying and filtering out unwanted or unsolicited emails. Spam filters use a variety of techniques to identify spam, including keyword filtering, Bayesian filtering, and machine learning.
Pattern detection is the process of identifying patterns in data. Pattern detection is used in a variety of applications, including fraud detection, anomaly detection, and trend analysis.
Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP is used in a variety of applications, including machine translation, text summarization, and question answering.
Image classification is the process of assigning a label to an image. Image classification is used in a variety of applications, including face recognition, object detection, and scene understanding.
Real-time visual tracking is the process of tracking the movement of objects in a video stream. Real-time visual tracking is used in a variety of applications, including video surveillance, autonomous driving, and sports analytics.
The other options are not supervised learning applications. Autonomous car driving and logistic optimization are examples of reinforcement learning. Bioinformatics and speech recognition are examples of unsupervised learning.