Machine Learning Fundamentals
Expert-defined terms from the Professional Certificate in AI for Chemical Engineering course at UK School of Management. Free to read, free to share, paired with a globally recognised certification pathway.
Machine Learning Fundamentals #
Machine Learning Fundamentals
Machine learning is a subset of artificial intelligence that focuses on developi… #
In the context of the Professional Certificate in AI for Chemical Engineering, understanding the fundamentals of machine learning is crucial for leveraging AI technologies to optimize chemical processes and enhance overall efficiency.
1. Supervised Learning #
A type of machine learning where the model is trained on labeled data, meaning the input data is paired with the correct output. The algorithm learns to map input data to the correct output during training, allowing it to make predictions on new, unseen data.
2. Unsupervised Learning #
In unsupervised learning, the model is trained on unlabeled data, meaning there is no predefined output. The algorithm learns to find patterns and relationships in the data without guidance, making it useful for tasks such as clustering and dimensionality reduction.
3. Reinforcement Learning #
A type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn the optimal strategy over time.
4. Feature Engineering #
The process of selecting and transforming input variables (features) to improve the performance of a machine learning model. Feature engineering plays a crucial role in building accurate and robust models.
5. Model Evaluation #
The process of assessing the performance of a machine learning model on unseen data. Common metrics for model evaluation include accuracy, precision, recall, F1 score, and ROC-AUC.
6. Hyperparameter Tuning #
The process of optimizing the hyperparameters of a machine learning algorithm to improve its performance. Hyperparameters are settings that are not learned by the model during training, such as learning rate, number of hidden layers, and batch size.
7. Overfitting #
When a machine learning model performs well on the training data but poorly on new, unseen data. Overfitting occurs when the model learns noise in the training data rather than the underlying patterns, leading to poor generalization.
8. Underfitting #
The opposite of overfitting, underfitting occurs when a model is too simple to capture the underlying patterns in the data. An underfit model has high bias and low variance, resulting in poor performance on both training and test data.
9. Cross #
Validation: A technique used to assess the performance of a machine learning model by splitting the data into multiple subsets (folds). The model is trained on several folds and evaluated on the remaining fold, allowing for a more reliable estimate of performance.
10. Feature Importance #
A measure of the contribution of each feature to the predictive power of a machine learning model. Understanding feature importance can help identify the most relevant variables and improve the interpretability of the model.
11. Ensemble Learning #
A machine learning technique that combines multiple models to improve performance. Common ensemble methods include bagging (e.g., random forests), boosting (e.g., AdaBoost), and stacking.
12. Gradient Descent #
An optimization algorithm used to minimize the loss function of a machine learning model by iteratively updating the model parameters in the direction of the steepest descent. Gradient descent is a fundamental technique for training neural networks and other complex models.
13. Deep Learning #
A subfield of machine learning that focuses on neural networks with multiple layers (deep neural networks). Deep learning has revolutionized various fields, including computer vision, natural language processing, and speech recognition.
14. Convolutional Neural Network (CNN) #
A type of neural network architecture commonly used for image recognition tasks. CNNs are designed to capture spatial hierarchies in the input data by using convolutional layers, pooling layers, and fully connected layers.
15. Recurrent Neural Network (RNN) #
A type of neural network architecture that is well-suited for sequential data, such as time series and natural language. RNNs have recurrent connections that allow them to capture temporal dependencies in the input data.
By mastering the fundamentals of machine learning, chemical engineers can levera… #
However, challenges such as data quality, interpretability, and ethical considerations must be carefully addressed to ensure the successful implementation of machine learning solutions in chemical engineering applications.