General Machine Learning

Part: Computer Science · Chapter: General Machine Learning · Edition: 2026-07-12
Keywords: supervised learning, generalization, optimization, model selection
Glossary: supervised learning, generalization, loss function, overfitting — see GLOSSARY

At a glance

General machine learning covers models and learning procedures, from perceptrons to ensembles and neural networks.
The chapter is an index of those distinctions.
It is orientation, not a training course and not a library of production recipes.

Online references (live)

Offline snapshots (link-rot shield)

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Machine learning

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Supervised learning

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Overfitting

Applications at Keyne Digital Assets

Classification, representation and model choice come up whenever we turn observations into structure. This chapter orients that vocabulary. It is not a claim that every model listed here is used in Situation Monitor.

Summary

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Preface

Acknowledgements
Historical Overview of Machine Learning

Chapter 1: Introduction to Machine Learning

Definition and Scope
Evolution from Ancient to Modern Techniques
Overview of Machine Learning Types (Supervised, Unsupervised, Reinforcement)

Chapter 2: Foundations of Machine Learning

Statistical Learning Theory
Probability Theory and Bayesian Inference
Linear Algebra and Multivariate Calculus
Optimization Techniques

Chapter 3: Classical Machine Learning Algorithms

Linear Regression
Logistic Regression
Decision Trees and Random Forests
Naive Bayes Classifier
Support Vector Machines (SVM)

Chapter 4: Ensemble Methods

Bagging and Boosting
Gradient Boosting Machines (GBM)
Adaptive Boosting (AdaBoost)
Stacking

Chapter 5: Neural Networks and Deep Learning

Perceptrons and Backpropagation
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)

Chapter 6: Natural Language Processing (NLP)

Text Preprocessing and Feature Extraction
Word Embeddings (Word2Vec, GloVe)
Sentiment Analysis
Machine Translation
Language Models (BERT, GPT)

Chapter 7: Reinforcement Learning

Markov Decision Processes
Q-Learning and SARSA
Deep Q-Networks (DQN)
Policy Gradient Methods
Applications in Game Theory and Robotics

Chapter 8: Time Series Analysis

ARIMA Models
State Space Models
GARCH Models for Volatility Forecasting
Anomaly Detection in Time Series

Chapter 9: Advanced Topics in Machine Learning

Feature Selection and Dimensionality Reduction
Hyperparameter Tuning and Model Selection
Explainable AI (XAI)
Federated Learning

Chapter 10: Machine Learning in Practice

Data Preprocessing and Wrangling
Model Deployment and Monitoring
Performance Metrics and Validation
Ethical Considerations and Bias Mitigation

Chapter 11: Special Topics in Trading with Machine Learning

Algorithmic Execution Strategies
High-Frequency Trading Algorithms
Quantitative Analysis and Statistical Arbitrage
Graphical Analysis and Technical Indicators
Sentiment Indicators and Market Psychology

Chapter 12: Future Trends and Directions

Quantum Machine Learning
Neuroevolution and Genetic Algorithms
Machine Learning in Edge Computing
AI in Healthcare and Personalized Medicine

Appendix

Mathematical Notation and Definitions
Data Sets and Resources for Machine Learning
Open-source Machine Learning Frameworks

Glossary of Terms

References and Further Reading

Historical Overview of Machine Learning

Machine learning has a rich history that spans several decades, with roots in various fields such as computer science, mathematics, and artificial intelligence (AI). Here’s a historical overview of the development of machine learning:

Early Foundations (1940s - 1950s):
* The foundations of machine learning can be traced back to the work of Alan Turing, who developed the concept of a universal machine capable of performing any computation. His ideas laid the groundwork for the theoretical underpinnings of machine learning.
Perceptrons (1950s - 1960s):
* In the late 1950s and early 1960s, Frank Rosenblatt introduced the concept of the perceptron, a simple computational model inspired by biological neurons. Perceptrons were capable of learning simple binary classifications and were used in early pattern recognition tasks.
The AI Winter (1970s - 1980s):
* During this period, progress in AI and machine learning was slow, leading to what is known as the “AI winter.” High expectations and limited computational power hindered the development of more sophisticated machine learning algorithms.
Backpropagation and Neural Networks Resurgence (1980s - 1990s):
* In the 1980s, the backpropagation algorithm for training artificial neural networks was rediscovered, allowing for the training of deeper networks. This marked the resurgence of interest in neural networks and machine learning.
Support Vector Machines (1990s):
* In the 1990s, Vladimir Vapnik and Corinna Cortes introduced support vector machines (SVMs), a powerful machine learning algorithm for classification and regression tasks. SVMs gained popularity for their ability to handle high-dimensional data.
Rise of Decision Trees and Random Forests (1990s - 2000s):
* Decision trees and ensemble methods like random forests gained prominence for their ability to handle complex data and provide interpretable models. These techniques were widely used in various applications, including classification and regression.
Deep Learning Resurgence (Late 2000s - Present):
* Deep learning, a subfield of machine learning focused on neural networks with many layers (deep neural networks), has experienced a significant resurgence. Breakthroughs in training deep networks, such as convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for sequential data, have revolutionized fields like computer vision, natural language processing, and speech recognition.
Big Data and Scalability (2010s - Present):
* The availability of massive datasets and increased computational power, along with distributed computing frameworks like Hadoop and Spark, has enabled machine learning models to scale to handle large-scale data.
Advances in Reinforcement Learning (2010s - Present):
* Reinforcement learning, a type of machine learning that focuses on learning from interactions with an environment, has seen significant advances. Deep reinforcement learning algorithms have achieved remarkable results in areas like robotics and game playing.
Ethical and Societal Considerations (Present):
- As machine learning and AI have become more pervasive, there is growing awareness of ethical and societal implications. Issues related to bias, fairness, privacy, and accountability are central to ongoing discussions in the field.

Machine learning continues to evolve rapidly, with ongoing research into new algorithms, techniques, and applications. It plays a crucial role in various industries, from healthcare to finance, and continues to shape the future of technology and artificial intelligence.

The Turing Machine

Alan Turing’s most significant contribution to the field of computer science and artificial intelligence was his concept of the “Universal Turing Machine.” The main catch or insight of Turing’s theory was the idea that a single, universal machine could be capable of simulating the behavior of any other machine, given the appropriate program or set of instructions.

Here are the key points and the main catch of Alan Turing’s theory:

Universal Machine: Turing proposed the concept of a hypothetical computing machine, now known as the “Turing machine.” The Turing machine was a mathematical abstraction consisting of an infinite tape, a read/write head, and a finite set of states. It could perform simple operations like reading, writing, and moving left or right on the tape.
Computability: Turing showed that this simple theoretical machine could, in principle, compute anything that any other computing machine could compute. In other words, it could solve any problem that was algorithmically solvable. This concept laid the foundation for the theory of computability and the idea of algorithmic computation.
Church-Turing Thesis: Turing’s work, along with the independent work of Alonzo Church, led to the Church-Turing thesis, which posits that any computation that can be algorithmically performed can be done by a Turing machine (or equivalently, by an algorithm). This thesis has become a fundamental principle in computer science and the theory of computation.
Universality: The key insight was that by changing the instructions on the tape, a single Turing machine could simulate the behavior of any other Turing machine, effectively making it a “universal” machine. This universality concept demonstrated that a single, general-purpose machine could perform any computation, given the right program.
Limitations of Computation: Turing’s work also had implications for understanding the limits of computation. He famously showed that there were problems, such as the Halting Problem, for which no algorithmic solution exists. This concept of undecidability highlighted the existence of inherently unsolvable problems in computation.

In summary, the main catch of Alan Turing’s theory was the concept of the Universal Turing Machine, which demonstrated that a single machine, with the right program, could simulate the behavior of any other machine and compute anything that is algorithmically computable. This idea laid the theoretical foundation for the development of modern computers and the theory of computation. It also established the Church-Turing thesis as a fundamental principle in computer science.

Frank Rosenblatt’s Perceptron

Frank Rosenblatt’s perceptron was a pioneering computational model inspired by the functioning of biological neurons. It was developed in the late 1950s and represented an early attempt at creating a machine capable of learning from data. The notes below elaborate on Frank Rosenblatt’s perceptron:

1. Biological Inspiration:

The perceptron was inspired by the way neurons in the brain work. Neurons receive input signals through dendrites, process them in the cell body, and produce an output signal through the axon. The perceptron attempted to mimic this process with artificial components.

2. Components of the Perceptron:

The perceptron consisted of:
Input Layer: This layer received input signals, typically represented as binary values (0 or 1).
Weights: Each input connection had an associated weight, which represented the strength of the connection.
Summation Function: The inputs were linearly combined using the weighted sum, which was computed as the dot product of the input vector and the weight vector.
Activation Function: The perceptron used a threshold activation function. If the weighted sum exceeded a certain threshold, the perceptron would output a 1; otherwise, it would output a 0.

3. Learning Rule:

The key innovation in the perceptron was the learning rule, which allowed it to adjust its weights based on errors in its output. The perceptron learning rule was a form of supervised learning:
If the perceptron’s output was correct, no changes were made to the weights.
If the output was incorrect, the weights were adjusted to reduce the error. This adjustment was proportional to the difference between the desired output and the actual output, and it was applied to all input connections.

4. Limitations of the Perceptron:

While the perceptron was a significant advancement, it had limitations:
Linearity: The perceptron could only learn linearly separable functions. It could not solve problems that required nonlinear decision boundaries.
XOR Problem: One of the most famous limitations of the perceptron was its inability to learn the XOR function, which has a nonlinear decision boundary.
Single-Layer Structure: The perceptron was limited to single-layer networks, which made it inadequate for tackling complex problems.

5. Impact and Historical Significance:

Despite its limitations, Frank Rosenblatt’s perceptron laid the foundation for future developments in neural networks and machine learning.
It inspired further research in the field of artificial neural networks and the development of more advanced algorithms, such as multi-layer perceptrons (MLPs), which could handle nonlinear problems.

6. Revival of Interest:

The perceptron’s limitations and the subsequent “AI winter” temporarily dampened enthusiasm for neural networks and machine learning. However, interest was revived in the 1980s with the development of backpropagation, which allowed for training multi-layer neural networks and marked the beginning of modern neural network research.

In summary, Frank Rosenblatt’s perceptron was a pioneering neural network model that introduced the idea of learning from data and adjusting weights to improve performance. While it had limitations and was limited to linearly separable problems, it played a crucial role in the history of artificial neural networks and inspired further research that ultimately led to the development of more powerful neural network architectures.

Backpropagation and Neural Networks

The rediscovery of the backpropagation algorithm in the 1980s marked a significant turning point in the history of artificial neural networks and had a profound impact on the resurgence of interest in neural networks and machine learning. Here’s an elaboration on this important development:

1. Background:

Artificial neural networks (ANNs) had been proposed earlier, with the perceptron being one of the earliest models in the 1950s. However, the perceptron and other single-layer neural networks had limitations, especially in their inability to handle complex, nonlinear problems.

2. The Challenge:

One of the major challenges in training multi-layer neural networks (also known as multi-layer perceptrons or MLPs) was finding an efficient way to adjust the weights of the connections between neurons to minimize errors in the network’s predictions.
Training such networks required solving the credit assignment problem—determining how to attribute errors at the output layer back to the hidden layers and adjusting weights accordingly.

3. Rediscovery of Backpropagation:

The backpropagation algorithm, short for “backward propagation of errors,” had actually been introduced in the 1960s by researchers like Stuart Dreyfus and David E. Rumelhart. However, it did not receive widespread attention at the time.
In the early 1980s, the backpropagation algorithm was independently rediscovered and popularized by several researchers, including Geoffrey Hinton, David Rumelhart, and Ronald Williams. They recognized its potential for training deep, multi-layer neural networks.

4. How Backpropagation Works:

Backpropagation is a supervised learning algorithm that adjusts the weights of neural network connections to minimize the difference between the network’s predicted outputs and the desired target values.
It involves two main phases:
Forward Pass: During this phase, input data is passed through the network layer by layer, and the network’s output is computed.
Backward Pass: In this phase, errors are propagated backward from the output layer to the hidden layers. The gradient of the error with respect to the network’s weights is computed, and weights are updated using gradient descent or a similar optimization technique.

5. Impact and Resurgence:

The rediscovery of backpropagation made it possible to train deeper neural networks, which could learn complex, hierarchical representations of data.
This marked the resurgence of interest in neural networks and machine learning because it opened the door to solving a wide range of problems that had previously been challenging or impossible for traditional algorithms.
Neural networks became particularly effective in tasks such as image recognition, speech recognition, and natural language processing.

6. Ongoing Developments:

Since the 1980s, research in neural networks and deep learning has continued to advance rapidly. Deep learning architectures, such as convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for sequential data, have been developed and refined.
Advances in hardware, particularly graphical processing units (GPUs), have accelerated the training of deep neural networks, making them more practical and efficient.

In conclusion, the rediscovery and popularization of the backpropagation algorithm in the 1980s were instrumental in enabling the training of deeper neural networks, which in turn led to the resurgence of interest in neural networks and machine learning. This development laid the foundation for the deep learning revolution, which has had a transformative impact on various fields of artificial intelligence and computer science.

Support vector machines

A Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for classification and regression tasks. It is particularly effective for tasks where the goal is to find a decision boundary that best separates data into distinct classes or to predict a continuous target variable. SVMs are known for their ability to handle high-dimensional data and complex decision boundaries. Here’s an elaboration on support vector machines:

1. Linear Separation:

At its core, an SVM seeks to find a hyperplane that best separates the data into two classes in a way that maximizes the margin between the two classes. This hyperplane is often referred to as the “decision boundary.”

2. Margin:

The margin is the distance between the decision boundary and the nearest data points from each class. SVM aims to maximize this margin. The rationale behind this is that a larger margin implies greater confidence in the classifier’s predictions and better generalization to unseen data.

3. Support Vectors:

The data points that are closest to the decision boundary and influence the placement of the boundary are called “support vectors.” These are the critical data points that determine the orientation and position of the hyperplane.

4. Linear and Non-Linear SVMs:

SVMs can be used for both linear and non-linear classification tasks:
Linear SVM: When the data is linearly separable, a linear SVM finds the optimal hyperplane in the original feature space.
Non-Linear SVM: When the data is not linearly separable, SVM can be extended to use kernel functions, such as polynomial or radial basis function (RBF) kernels, to map the data into a higher-dimensional space where it becomes linearly separable. In this transformed space, a linear hyperplane is found.

5. Kernel Trick:

The kernel trick is a fundamental concept in SVMs. It allows SVMs to operate in a high-dimensional feature space without explicitly transforming the data into that space, which can be computationally expensive. Kernel functions efficiently compute the dot product between data points in the high-dimensional space.

6. C Parameter:

SVMs have a regularization parameter, often denoted as “C.” The C parameter controls the trade-off between maximizing the margin and minimizing classification errors. A small C value will result in a wider margin but may allow some misclassifications, while a large C value will lead to a narrower margin but fewer misclassifications.

7. Applications:

SVMs are widely used in various applications, including:
Text classification and natural language processing (NLP).
Image classification and object detection.
Bioinformatics for gene classification and protein folding.
Handwriting recognition.
Financial forecasting and anomaly detection.
Medical diagnosis.

8. Advantages:

SVMs are effective in high-dimensional spaces and can handle complex decision boundaries.
They have strong theoretical foundations and are less prone to overfitting when appropriately regularized.
SVMs work well with both linear and non-linear data.

9. Limitations:

SVMs can be computationally expensive, especially for large datasets.
Choosing an appropriate kernel and tuning hyperparameters can be challenging.
SVMs may not perform as well when the number of features is much larger than the number of samples.

In summary, Support Vector Machines are a versatile and powerful machine learning algorithm used for classification and regression tasks. They are known for their ability to find optimal decision boundaries that maximize the margin between classes and can be applied to a wide range of real-world problems. Properly configured SVMs can yield highly accurate results in various domains.

Difference between a SVM and a Neural Network

Support Vector Machines (SVMs) are not a type of neural network. SVMs and neural networks are distinct machine learning algorithms, although they are both used for various classification and regression tasks. Here are some key differences between SVMs and neural networks:

Algorithmic Approach:
* SVMs: SVMs are based on the idea of finding a hyperplane that best separates data into distinct classes while maximizing the margin between them. They focus on finding an optimal decision boundary.
* Neural Networks, on the other hand, are composed of interconnected artificial neurons organized in layers. They are designed to learn complex patterns and relationships within data through a series of weighted connections and activation functions.
Model Complexity:
* SVMs: SVMs are typically considered simpler models compared to neural networks. They aim to find a linear or nonlinear decision boundary in the feature space.
* Neural Networks, especially deep neural networks, have multiple hidden layers and can capture highly complex, nonlinear relationships in the data. They are capable of learning hierarchical representations.
Training Paradigm:
* SVMs: SVMs use a margin-based approach for training. The goal is to maximize the margin between classes while minimizing classification errors.
* Neural Networks use a supervised learning approach with backpropagation and gradient descent (or variants) for training. They adjust weights and biases to minimize a specified loss function.
Representation:
* SVMs: SVMs don’t have the same layered architecture as neural networks. They typically consist of a single decision boundary.
* Neural Networks consist of multiple layers, including an input layer, one or more hidden layers, and an output layer. The connections and transformations between layers allow for the learning of complex features.
Applications:
* SVMs: SVMs are often preferred when dealing with small to medium-sized datasets, high-dimensional data, and tasks where interpretability and margin maximization are important. They are used in areas like image classification, text classification, and bioinformatics.
* Neural Networks, especially deep learning models, excel in tasks involving large datasets, such as image recognition, speech recognition, natural language processing, and complex pattern recognition.

While SVMs and neural networks are distinct approaches, there can be overlap in their application domains. Some researchers have explored hybrid models that combine elements of both SVMs and neural networks to leverage the strengths of each approach. However, it’s essential to recognize that they represent different paradigms in machine learning and have unique characteristics and use cases.

Decision Trees and Random Forests

Decision trees and ensemble methods, such as random forests, have gained prominence in the field of machine learning due to their effectiveness in handling complex data and their ability to provide interpretable models. These techniques have found widespread use in various applications, including classification and regression tasks. Here’s an elaboration on this:

1. Decision Trees:

Decision trees are a supervised machine learning algorithm used for both classification and regression tasks. They model decisions as a tree-like structure, where each internal node represents a test on a feature, each branch represents an outcome of the test, and each leaf node represents a class label or a numeric value.
Decision trees are highly interpretable, as they mimic human decision-making processes by following a sequence of simple if-then-else rules.

2. Interpretable Models:

Decision trees provide transparent and interpretable models that can be easily understood by domain experts and stakeholders. The if-then-else rules can be visually represented, making it clear how the model arrived at a particular decision.

3. Handling Complex Data:

Decision trees are capable of handling complex and non-linear relationships in data. They can capture interactions between features and identify important features for making decisions.
Decision trees do not assume linear relationships between variables, making them suitable for a wide range of data types and distributions.

4. Limitations of Decision Trees:

Decision trees can be prone to overfitting, especially when they are deep and complex. Overfitting occurs when the tree captures noise in the training data, leading to poor generalization to unseen data.
To mitigate overfitting, techniques such as pruning and setting maximum tree depth are often used.

5. Ensemble Methods and Random Forests:

Ensemble methods are techniques that combine the predictions of multiple base models to improve overall performance and robustness.
Random forests are an ensemble method that builds multiple decision trees and combines their predictions. Each tree is trained on a random subset of the data, and feature subsets, to ensure diversity among the trees.
Random forests address the overfitting problem associated with individual decision trees. By aggregating the predictions of multiple trees, they reduce variance and improve predictive accuracy.

6. Advantages of Random Forests:

Random forests offer high predictive accuracy, robustness to outliers and noise, and reduced overfitting compared to single decision trees.
They can handle high-dimensional data and large datasets effectively.
Feature importance can be readily obtained from random forests, helping to identify the most influential features in the data.

7. Applications:

Decision trees and random forests have been applied in a wide range of fields, including:
Healthcare: Diagnosis of diseases based on patient data.
Finance: Credit risk assessment and fraud detection.
Marketing: Customer segmentation and churn prediction.
Ecology: Species classification based on environmental factors.
Natural language processing: Sentiment analysis and text classification.

8. Other Ensemble Methods:

In addition to random forests, other ensemble methods like gradient boosting, AdaBoost, and bagging have also gained popularity for improving model performance and robustness.

In summary, decision trees and ensemble methods, particularly random forests, have become valuable tools in machine learning for their ability to handle complex data, provide interpretable models, and improve predictive accuracy. Their versatility and interpretability make them suitable for a wide range of applications where understanding the decision-making process is essential.

Artificial Neural Networks

RNN: LSTM Optimizations

There are several automated techniques and libraries available that can help you automate the hyperparameter tuning process, making it more efficient and less time-consuming. Here are some common approaches to automate hyperparameter tuning:

Grid Search and Random Search: These are basic but effective techniques for hyperparameter tuning. In grid search, you specify a grid of hyperparameter values to explore, and the algorithm trains and evaluates models for each combination of hyperparameters. In random search, random combinations of hyperparameters are sampled and evaluated. Scikit-learn provides GridSearchCV and RandomizedSearchCV classes for these purposes.
Bayesian Optimization: Bayesian optimization is a more sophisticated technique that uses probabilistic models to predict which hyperparameter combinations are likely to yield the best results. Libraries like BayesianOptimization, scikit-optimize, and hyperopt can be used for this purpose.
Hyperparameter Tuning Libraries: Several libraries are designed specifically for automating hyperparameter tuning. Some popular ones include:
* Keras Tuner: This library is integrated with Keras and can be used to search for the best hyperparameters for deep learning models.
* Optuna: Optuna is a versatile, distributed hyperparameter optimization framework that can be used with various machine learning libraries.
* Ray Tune: Ray Tune is a scalable and distributed hyperparameter tuning library that supports both search algorithms and distributed computation.
AutoML Platforms: AutoML (Automated Machine Learning) platforms like Google AutoML, H2O.ai, and Databricks AutoML provide end-to-end automation for machine learning tasks, including hyperparameter tuning. These platforms often include automated feature engineering and model selection in addition to hyperparameter tuning.
Deep Learning Frameworks: Deep learning frameworks like TensorFlow and PyTorch offer built-in tools for hyperparameter tuning. For example, TensorFlow’s Keras Tuner, and PyTorch’s Ignite offer convenient APIs for hyperparameter search.
Cloud-Based Services: Cloud providers like Google Cloud AI Platform, Amazon SageMaker, and Microsoft Azure Machine Learning offer managed services that include automated hyperparameter tuning as part of their machine learning pipelines.

Here’s a general workflow for automating hyperparameter tuning:

Define the search space: Specify the hyperparameters to be tuned and their respective search ranges or distributions.
Choose a tuning strategy: Select the automated tuning technique or library that best suits your needs and the available computational resources.
Define the optimization objective: Determine the metric you want to optimize (e.g., validation loss, accuracy, F1 score).
Run the hyperparameter search: Use the chosen library or approach to search for the best hyperparameters. The search process will train and evaluate multiple models with different hyperparameter configurations.
Evaluate and select the best model: Once the search is complete, evaluate the models based on the optimization objective and select the best-performing model.
Fine-tune and deploy: After finding the best hyperparameters, you can fine-tune the model with these settings, and then deploy it for inference.

Automating hyperparameter tuning saves time and helps you find better-performing models by systematically exploring a wide range of hyperparameter combinations. It’s particularly valuable in deep learning and complex machine learning tasks where manual tuning can be impractical.

Glossary & References

Transformer (machine learning model)

advancements in machine learning for

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