What is the difference between supervised, unsupervised, and reinforcement learning?
Supervised Learning: The model is trained on labeled data, meaning that the input and output data are known. The goal is to learn a mapping from inputs to outputs. Example: Image classification.
Unsupervised Learning: The model is trained on unlabeled data. The goal is to find patterns and structures in the data. Example: Clustering.
Reinforcement Learning: The model learns by interacting with an environment. It receives rewards or penalties for its actions and learns to maximize its cumulative reward. Example: Training a bot to play a game.
Explain overfitting and underfitting. How do you address them?
Overfitting: Occurs when a model learns the training data too well, including the noise. It performs well on the training data but poorly on new, unseen data.
Underfitting: Occurs when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training data and new data.
How to address them:
- Overfitting: Use more data, cross-validation, regularization, or feature selection.
- Underfitting: Use a more complex model, add more features, or reduce regularization.
What are neural networks, and how do they learn?
Neural Networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They are made up of layers of interconnected nodes, or neurons. Each connection has a weight associated with it.
Neural networks learn by adjusting the weights of the connections between neurons. This is typically done through a process called backpropagation, where the error between the network's output and the desired output is propagated back through the network to update the weights.
Explain the concept of feature engineering in ML.
Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. It involves selecting, transforming, and creating features from raw data to improve the performance of a model.
Compare decision trees, random forests, and gradient boosting.
Decision Trees: A simple model that is easy to interpret but can be prone to overfitting.
Random Forests: An ensemble of decision trees that improves performance and reduces overfitting by averaging the results of many trees.
Gradient Boosting: An ensemble method that builds trees one at a time, where each new tree helps to correct errors made by previously trained trees. It is often the highest-performing of the three.
How does natural language processing (NLP) work?
Natural Language Processing (NLP) is a field of AI that gives computers the ability to understand, interpret, and generate human language. It involves several steps, including:
- Tokenization: Breaking down text into individual words or tokens.
- Parsing: Analyzing the grammatical structure of a sentence.
- Semantic Analysis: Understanding the meaning of the text.
What are ethical concerns in AI research and deployment?
Some of the key ethical concerns in AI include:
- Bias: AI systems can perpetuate and even amplify human biases that are present in the data they are trained on.
- Privacy: AI systems often require large amounts of data, which can raise privacy concerns.
- Accountability: It can be difficult to determine who is responsible when an AI system makes a mistake.
- Impact on Employment: AI could automate many jobs, leading to widespread unemployment.