1. Machine learning: Machine learning is a subset of artificial intelligence that focuses on developing algorithms and statistical models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. Common techniques in machine learning include supervised learning, unsupervised learning, and reinforcement learning. 2. Genetic algorithms: Genetic algorithms are a type of optimization algorithm inspired by the process of natural selection. They use principles of genetics, such as mutation, crossover, and selection, to create a population of solutions to a problem and iteratively improve upon them over multiple generations. 3. Data mining: Data mining is the process of extracting patterns, trends, and insights from large datasets using a combination of statistical techniques, machine learning algorithms, and computer science methods. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. 4. Neural networks: Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain. They consist of interconnected layers of artificial neurons that can learn complex patterns in data and make decisions or predictions. Common types of neural networks include feedforward neural networks, convolutional neural networks, and recurrent neural networks. 5. Computer vision: Computer vision is a field of study that focuses on enabling computers to interpret and understand visual information from the real world. Techniques in computer vision include image segmentation, object detection, image classification, and optical character recognition. 6. Natural language processing: Natural language processing is a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. Techniques in natural language processing include sentiment analysis, named entity recognition, machine translation, and question-answering systems.