Machine learning is a vast and rapidly growing field, and there are many active research areas and tasks within it. This includes supervised, unsupervised, and reinforcement learning algorithms, deep learning and neural networks, and more.
Major Research Areas: Feature Engineering, Model Development, Model Selection, Hyperparameter Tuning, Regularization.
Deep learning is a subfield of machine learning that involves training artificial neural networks with many layers to perform various predictive tasks and the field is constantly evolving and growing as new advances are made.
Major Research Areas: Architecture Design, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Model Compression, Reinforcement Learning, Unsupervised Learning, Autoencoders.
Computer vision is a field that focuses on enabling computers to interpret and understand visual information in the same way that humans do. This field focuses on enabling machines to understand, analyze, and interpret visual data from the world.
Major Research Areas: Object Detection and Recognition, Image Segmentation, Image Restoration, Image Generation, Scene Understanding, 3D Vision, Motion Analysis, Augmented Reality.
Natural language processing (NLP) is a field that focuses on enabling computers to understand, generate, and process human language. This area deals with enabling machines to understand, interpret, and generate human language.
Major Research Areas: Text Classification, Named Entity Recognition, Part-of-Speech Tagging, Dependency Parsing, Machine Translation, Sentiment Analysis, Question Answering, Text Generation.
Generative Adversarial Networks (GANs) are a class of deep learning models that involve two neural networks competing against each other to generate synthetic data that is similar to real data. This is a class of machine learning models that uses a two-part model consisting of a generator and a discriminator to generate new data samples.
Major Research Areas: Model Architecture, Model Stability, Data Generation Quality, Semi-Supervised Learning, Adversarial Examples, Interpretability, Domain Adaptation.
Explainable AI (XAI) is a field that focuses on developing AI systems that are transparent, interpretable, and able to provide understandable explanations for their decisions and actions. This is an area of research that focuses on developing AI systems that can provide clear and understandable explanations for their predictions and actions.
Major Research Areas: Model Interpretability, Human-AI Interaction, Model Explanations, Fairness and Bias, Trust and Safety, Regulation and Policy, Ethical Considerations.
Time series analysis is a field that focuses on the analysis and modelling of data/signals that is collected over time.
Major Research Areas: Prediction, Disease Diagnosis, Trend Analysis, Seasonality Analysis, Anomaly Detection, Time Series Decomposition, Time Series Modelling, Time Series Clustering, Time Series Causality Analysis.