Machine learning

Machine learning (ML) is a subset of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computers to perform specific tasks without explicit instructions. Instead, these systems learn from data by identifying patterns and making decisions based on the information they process.

Machine learning can be broadly categorized into three types:

1. Supervised Learning: The algorithm is trained on labeled data, meaning the input comes with the corresponding output. The system learns to map inputs to outputs based on the training data. Common applications include classification and regression tasks.

2. Unsupervised Learning: The algorithm is used on unlabeled data, meaning it must find patterns and relationships in the data without specific instructions on what to look for. Common applications include clustering and dimensionality reduction.

3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or punishments and makes decisions to maximize cumulative rewards over time.

Key Concepts in Machine Learning

• Training and Testing: Data is typically split into training and testing sets. The training set is used to train the model, while the testing set is used to evaluate its performance.

• Features and Labels: Features are the input variables used to make predictions, and labels are the output variables that the model aims to predict.

• Overfitting and Underfitting: Overfitting occurs when a model learns the training data too well, including noise and outliers, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

• Model Evaluation: Common metrics for evaluating machine learning models include accuracy, precision, recall, F1 score, and mean squared error (MSE).

  Literature References

  Bishop, C. M. (2006). "Pattern Recognition and Machine Learning." Springer.
  Goodfellow, I., Bengio, Y., & Courville, A. (2016). "Deep Learning." MIT Press.
  Hastie, T., Tibshirani, R., & Friedman, J. (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction." Springer.
  Murphy, K. P. (2012). "Machine Learning: A Probabilistic Perspective." MIT Press.

Deep learning

Deep learning is a subset of machine learning within artificial intelligence (AI) that involves neural networks with many layers (hence "deep") which can learn and make intelligent decisions on their own. These neural networks are designed to mimic the human brain, allowing the system to learn from vast amounts of data, identify patterns, and make decisions with minimal human intervention.

Literature References

Goodfellow, I., Bengio, Y., & Courville, A. (2016). "Deep Learning." MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). "Deep Learning." Nature, 521(7553), 436-444.
Schmidhuber, J. (2015). "Deep learning in neural networks: An overview." Neural Networks, 61, 85-117.
Russell, S., & Norvig, P. (2020). "Artificial Intelligence: A Modern Approach" (4th ed.). Pearson.