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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Unveiling the Essence of Stochastic in Machine Learning

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 11 Dec, 2023
5 min read

Introduction

Machine learning is a field that empowers computers to learn from data and make intelligent decisions. It encompasses various concepts and techniques. One such concept is “stochastic,” which plays a crucial role in many machine learning algorithms and models. In this article, we will delve into the meaning of stochastic in machine learning, explore its applications, and understand its significance in optimizing learning processes.

Table of Contents

Understanding Stochastic in Machine Learning

Stochastic, in the context of machine learning, refers to the introduction of randomness or probability into algorithms and models. It allows for the incorporation of uncertainty, enabling the algorithms to handle noisy or incomplete data effectively. By embracing stochasticity, machine learning algorithms can adapt to changing environments and make robust predictions.

Stochastic Processes in Machine Learning

Stochastic processes are mathematical models that describe the evolution of random variables over time. They are widely used in machine learning to model and analyze various phenomena. These processes possess unique characteristics that make them suitable for capturing the inherent randomness in data.

Types of stochastic processes in machine learning
Source: ResearchGate

Definition and Characteristics of Stochastic Processes

A stochastic process is a collection of random variables indexed by time or another parameter. It provides a mathematical framework to describe the probabilistic behavior of a system evolving over time. Stochastic processes exhibit properties such as stationarity, independence, and Markovianity, which enable them to capture complex dependency patterns in data.

Applications of Stochastic Processes in Machine Learning

Stochastic processes find applications in diverse areas of machine learning. They are helpful in time series analysis, where the goal is to predict future values based on past observations. They also play a crucial role in modeling and simulating complex systems, such as financial markets, biological processes, and natural language processing.

Stochastic Gradient Descent (SGD)

Stochastic Gradient Descent (SGD) is a popular optimization algorithm in machine learning. It is a variant of the traditional gradient descent algorithm that introduces randomness into the parameter updates. SGD is particularly useful when dealing with large datasets, as it allows for efficient and scalable optimization.

Stochastic Gradient Descent (SGD)

Overview of SGD

In SGD, instead of computing the gradient using the entire dataset, the gradient is estimated using a randomly selected subset of the data, known as a mini-batch. This random sampling introduces stochasticity into the optimization process, making it more adaptable to noisy or dynamic data. By iteratively updating the model parameters based on these mini-batch gradients, SGD converges to an optimal solution.

Advantages and Disadvantages of SGD

SGD offers several advantages over traditional gradient descent. It converges faster, requires less memory, and is computationally efficient, especially for large-scale datasets. However, SGD’s stochastic nature introduces some drawbacks. It may converge to suboptimal solutions due to the noise in the gradient estimates, and the learning rate needs careful tuning to ensure convergence.

Implementing SGD in Machine Learning Algorithms

SGD can be implemented in various machine learning algorithms, such as linear regression, logistic regression, and neural networks. In each case, the algorithm updates the model parameters based on the gradients computed from the mini-batches. This stochastic optimization technique enables the models to learn from massive datasets efficiently.

Stochastic Models in Machine Learning

Stochastic models are probabilistic models that capture the uncertainty in data and make predictions based on probability distributions. They are widely used in machine learning to model complex systems and generate realistic samples.

Stochastic Models in Machine Learning

Types of Stochastic Models in Machine Learning

There are 3 types of Stochastic models in machine learning: Hidden Markov Models, Gaussian Mixture Models, and Bayesian Networks. These models incorporate randomness and uncertainty, allowing for more accurate representation and prediction of real-world phenomena.

Let’s now explore the applications of these models.

  1. Hidden Markov Models (HMMs)
  • Application: Speech recognition
  • Use: Modeling the probabilistic nature of speech patterns
  1. Gaussian Mixture Models (GMMs)
  • Application: Image and video processing
  • Use: Modeling the statistical properties of pixels
  1. Bayesian Networks
  • Application: Medical diagnosis
  • Use: Capturing dependencies between symptoms and diseases

Stochastic Sampling Techniques

Stochastic sampling techniques are used to generate samples from complex probability distributions. These techniques play a vital role in tasks such as data generation, inference, and optimization.

Importance Sampling

Importance sampling is a technique for estimating properties of a target distribution by sampling from a different, easier-to-sample distribution. It allows for efficient estimation of expectations and probabilities, even when the target distribution is challenging to sample directly.

Markov Chain Monte Carlo (MCMC)

MCMC is a class of algorithms used to sample from complex probability distributions. It constructs a Markov chain that converges to the desired distribution, allowing for efficient sampling. MCMC methods, such as the Metropolis-Hastings algorithm and Gibbs sampling, are widely useful in Bayesian inference and optimization.

Stochastic Optimization Algorithms

Stochastic optimization algorithms are useful in finding optimal solutions in the presence of randomness or uncertainty. These algorithms mimic natural processes, such as annealing, evolution, and swarm behavior, to explore the solution space effectively.

Simulated Annealing

Simulated annealing is an optimization algorithm inspired by the annealing process in metallurgy. It starts with a high temperature, allowing for random exploration of the solution space, and gradually decreases the temperature to converge to the optimal solution. Simulated annealing is particularly useful for solving combinatorial optimization problems.

Genetic Algorithms

Genetic algorithms are optimization algorithms based on the process of natural selection and genetics. They maintain a population of candidate solutions and iteratively evolve them through selection, crossover, and mutation operations. Genetic algorithms are effective in solving complex optimization problems with large solution spaces.

Particle Swarm Optimization

Particle swarm optimization is an optimization algorithm based on the collective behavior of bird flocks or fish schools. It maintains a population of particles that move through the solution space, guided by their own best position and the best position found by the swarm. Particle swarm optimization is most useful in continuous optimization problems.

Ant Colony Optimization

Ant colony optimization is an optimization algorithm inspired by the foraging behavior of ants. It models the problem as a graph, where ants deposit pheromones to communicate and find the optimal path. Ant colony optimization is particularly useful for solving combinatorial optimization problems, such as the traveling salesman problem.

Stochasticity vs Determinism in Machine Learning

The choice between stochastic and deterministic approaches in machine learning depends on the problem at hand and the available data. Both approaches have their pros and cons, and their suitability varies across different scenarios.

Pros and Cons of Stochastic Approaches

Stochastic approaches, with their inherent randomness, allow for better adaptation to changing environments and noisy data. They can handle large-scale datasets efficiently and provide robust predictions. However, stochastic approaches may suffer from convergence issues and require careful tuning of hyperparameters.

Deterministic Approaches in Machine Learning

Deterministic approaches, on the other hand, provide more stable and predictable results. They are suitable for problems with noise-free data and well-defined underlying patterns. Deterministic approaches, such as traditional gradient descent, are useful in scenarios where interpretability and reproducibility are crucial.

Conclusion

Stochasticity plays a vital role in machine learning, enabling algorithms to handle uncertainty, adapt to changing environments, and make robust predictions. Stochastic processes, stochastic gradient descent, stochastic models, and stochastic optimization algorithms are essential components of the machine learning toolbox. By understanding and leveraging the power of stochasticity, we can unlock the full potential of machine learning in solving complex real-world problems.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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