Pattern Recognition: 

1. Computer Vision: Focuses on the interpretation of visual data, such as images and videos, to extract meaningful information.

2. Speech Recognition: Deals with the automatic transcription and understanding of spoken language.

3. Natural Language Processing: Involves the processing and analysis of human language, enabling machines to understand, generate, and interact with text or speech.

Machine Intelligence:

1. Reinforcement Learning: Concerned with developing algorithms that enable machines to learn through trial and error interactions with their environment.

2. Robotics: Focuses on the design, control, and development of physical robots capable of performing tasks autonomously or with minimal human intervention.

3. Cognitive Computing: Aims to create computer systems that mimic human cognitive processes, such as perception, reasoning, and decision-making.

Deep Learning:

1. Convolutional Neural Networks (CNNs): Specialized deep learning architectures designed for image and video analysis tasks.

2. Recurrent Neural Networks (RNNs): Neural networks with feedback connections, suitable for sequential and temporal data analysis, such as natural language processing and speech recognition.

3. Generative Adversarial Networks (GANs): Deep learning models that can generate new data samples, such as images or text, by learning from existing training data.

Biomedical Image Processing:

1. Medical Imaging Analysis: Focuses on developing algorithms for the analysis, segmentation, and interpretation of medical images obtained from modalities such as CT, MRI, and ultrasound.

2. Computer-Aided Diagnosis: Aims to assist healthcare professionals in diagnosing diseases by developing algorithms that can detect and classify abnormalities in medical images.

3. Image Registration: Concerned with aligning and combining multiple medical images to create a comprehensive representation for improved visualization and analysis.