"Machine learns with respect to a particular task T, performance metric P following experience E, if the system reliably improves its performance P at task T following experience E."


Machine learning

1. Fundamentals of ML

Basic principles of machine learning, including supervised and unsupervised learning, feature selection, model evaluation, and overfitting.

2. Supervised Algorithms

Machine learning algorithms that learn from labeled training data to make predictions or decisions, such as linear regression, decision trees, and support vector machines.

3. Gradient Descent Method

An optimization technique used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent.

4. MLE & MAP

MLE (Maximum Likelihood Estimation) estimates model parameters by maximizing the likelihood function, while MAP (Maximum A Posteriori) incorporates prior distributions into parameter estimation.

5. Linear Regression

A statistical method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.

6. Polynomial Regression

An extension of linear regression that models the relationship between the dependent variable and the independent variable(s) as an nth-degree polynomial.

7. Ridge-lasso-Elasticnet

Techniques combining regularization methods to prevent overfitting and improve model performance.

8. PC Analysis (PCA)

Principal Component Analysis is a technique for dimensionality reduction by transforming data into orthogonal components.

9. Classification Regression

Methods for classifying data into categories and predicting continuous values using regression techniques.

10. Logistic Regression

A classification algorithm used for binary outcomes, predicting probabilities based on the logistic function.

11. Naive Bayes ML

A probabilistic classifier based on Bayes' theorem with an assumption of feature independence.

12. KNN ML

K-Nearest Neighbors is a simple, non-parametric algorithm used for classification and regression based on proximity.

13. Decision Tree

A model that uses a tree-like graph of decisions and their possible consequences for classification and regression.

14. Support Vector

Support Vector Machines are used for classification and regression by finding the optimal hyperplane that maximizes the margin between classes.

Deep learning

1. Optimization Methods

Techniques used to find the best possible solution or outcome by adjusting variables to minimize or maximize a particular function, commonly used in machine learning and operations research.

2. Deep Learning

A subset of machine learning involving neural networks with many layers (deep neural networks) that can learn and model complex patterns in large amounts of data.

3. Activation Function

A function in a neural network that determines whether a neuron should be activated or not, helping the network to learn complex patterns.

4. Convolutional Neural Networks (CNNs)

A class of deep learning neural networks, primarily used for analyzing visual data, that employ convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.

9. Ensemble learning

Ensemble learning in machine learning refers to techniques that combine the predictions from multiple models (learners) to improve the overall performance.

Natural Language Processing (NLP) & Artificial intelligence (AI)

1. Natural Language Processing (NLP)

Statistics

1. Statistics

2. Descriptive statistics

3. Inferential statistics

4. Statistical Modeling

Applications

1. Applications: Data Mining

Reference

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