Machine learning : a hands-on approach
Robin, C R Rene
Machine learning : a hands-on approach C R Rene Robin, Doreen Robin, Chandra Mouli P V S S R - Kolkata University press c2025 - xiv, 804p. P.B.
Chapter 1 Introduction to Machine Learning
Introduction | What is Machine Learning? | History of Machine Learning | Role of Machine Learning in Computer Science and Problem Solving | Why Machine Learning? | Adaptivity of Machine Learning | Designing versus Learning | Training versus Testing in Machine Learning | Machine Learning versus Automation | Predictive and Descriptive Tasks in Machine Learning | Some Terminology Related to Machine Learning | Types of Machine Learning | Passive Learning versus Active Learning | Online versus Batch Machine Learning | Differences between Machine Learning Models and Algorithms
| Disadvantages of Data-driven Solutions | Well-posed Machine Learning Problems | Designing a Learning System (Life Cycle of Machine Learning)
Chapter 2 Probability Theory and Statistics in Machine Learning
Introduction | Probability | Probability Theory | Joint, Marginal, and Conditional Probability | Statistics | Key Concepts of Probability Distributions | Probability Distributions | Examples of Probability Distributions | Conditional Distribution | Joint Distribution | Combinatorics | Probability Rules and Axioms | Moment Generating Function | Maximum Likelihood Estimation | Density Functions | Density Estimation | Challenges and Future Directions in Probability and Statistics for Machine Learning
Chapter 3 Linear Algebra
Introduction | Linear Regression | Matrix Decomposition | Vectors and Matrices | Eigenvalue and Eigenvectors | Norms and Vector Spaces | Optimization | Linear Transformation | Cramer’s Rule | Gaussian Elimination | LU Decomposition | QR Decomposition | Eigen Decomposition | Symmetric Matrices | Orthogonalization | Deep Learning with Linear Algebra
Chapter 4 Algorithms and Complex Optimizations
Sets, Relations, and Functions | Convex Sets and Convex Functions | Optimization Problems | Convex Optimization | Unconstrained Optimization | Constrained Optimization | Dual Optimization Problems | Dynamic Programming | Sublinear Algorithms | Graphs | Transforms | Information Theory | Manifolds
Chapter 5 Computational Learning Theory
Introduction | Objectives of Computational Learning Theory | History | Importance of Computational Learning Theory | The Main Methods | Probably Approximately Correct Learning | Complexity Theory of Machine Learning | Mistake-bound Learning Model | Instance-based learning | Lazy and Eager Learning | Generative Learning | Consistent Learning | Worst Case (Online) Learning | Applications of Computational Learning Theory | Evaluation Metrics for Computational Learning Theory | Future Directions in Computational Learning Theory
Chapter 6 Machine Learning Models
Introduction | Models in Machine Learning | Features | Concept Learning
Chapter 7 Unsupervised Learning
Introduction | What and Why of Unsupervised Learning | Types of Unsupervised Learning | Markov Models | Hidden Markov Model | Matrix Factorization and Matrix Completion Models | Generative Models | Latent Factor Models | Inference Models | Non-negative Matrix Factorization | Advantages of Unsupervised Learning | Disadvantages of Unsupervised Learning
Chapter 8 Supervised Learning: Classification
Introduction | K-Nearest Neighbor (KNN) | Decision Trees | Random Forests | Linear Classifiers | Applications of Supervised Learning | Limitations and Challenges of Supervised Learning
Chapter 9 Supervised Learning: Regression
Introduction | Linear Regression versus Non-Linear Regression | Types of Linear Models | Least Squares Method (LSM) | Multivariate Linear Regression | Nonlinearity and Kernel Methods | Generalized Linear Models | AdaBoost (Adaptive Boosting) | Regularized Regression | Backpropagation | Support Vector Regression | Decision Tree Regression | Random Forest Regression | Neural Network Regression |
Multi-layer Propagation | Radial Basis Functions | Splines | Curse of Dimensionality | Interpolations and Basis Functions | Multi-class/Structured Outputs, Ranking
Chapter 10 Artificial Neural Networks
Introduction to Neural Networks | Introduction to Artificial Neural Networks | Types of Artificial Neural Networks | Other Types of ANNs | Building Neural Network Architectures | Training Neural Networks with Backpropagation | Autoencoders | Applications of ANNs | Future of ANNs
Chapter 11 Trends in Machine Learning
Reinforcement Learning | Multitask Learning | Online Learning | Sequence Learning | Prediction Learning | Bagging and Boosting in Machine Learning | Trends in Machine Learning Technology
Chapter 12 Applications of Machine Learning in Various Industries
Real-World Problems Solved by Machine Learning | Applications of Machine Learning in the Retail Industry | Applications of Machine Learning in the Logistics Industry | Applications of Machine Learning in the Manufacturing Industry | Applications of Machine Learning in the Energy and Utilities Industry | Applications of Machine Learning in the Travel Industry | Applications of Machine Learning in the Banking Industry | Applications of Machine Learning in the Finance Industry | Applications of Machine Learning in the Insurance Industry
Chapter 13 Machine Learning Programming: Capstone Projects Using Python and R
Introduction | Installing Python | The sklearn Package | Anaconda Navigator | Data Operations on the Iris Data Set | Finding Outliers | Removing Outliers | Imputing Null Values | Capping the Outlier Values | Splitting the Data into Training and Testing Data | Training and Evaluating the Model | Regularization Techniques that Prevent Overfitting | Implement Linear Regression | Implement Logistic Regression | Decision Tree Classifier | Implement SVM | Implement PCA | Implement Steepest Descent | Implement Random Forest | Implement Random Search | Implement Naïve Bayes | Implement Single-Layer Perceptron Learning Algorithm | Implement Radial Basis Functions | Implement Linear Classifier | Implement Bayesian Classifier | Implement K-Nearest Neighbor Classifier | Implement Linear Discriminant Analysis | Implement Locality Preserving Projection | Implement Logic Gates without Perceptron Model | Implement Logic Gates with Perceptron Model | Handwritten Classification using CNN | Introduction to R Programming
Chapter 14 Machine Learning Programming Using Jupyter Notebook
Introduction | Using the Online Interface of Jupyter Notebook | A Python Program that Demonstrates the Use of Data Types | A Python Program that Asks for User Input and Uses Conditional Statements to Respond with Different Outputs | A Python Program that Prints Out a Sequence of Numbers using a for Loop and then Asks the User to Do the Same with a while Loop | A Python Program that Defines a Function to Calculate the Area of a Circle, Given its Radius, and then Calls that Function with Different Values | A Python Program that Creates a List of Items and a Dictionary of Key–Value Pairs, and then Demonstrates How to Access and Modify Elements | A Python Program that Reads a Text File, Counts the Number of Words, and Writes the Result to a New File | A Python Program that Intentionally Raises an Error and then Catches it with a try-except Block, Printing an Informative Message to the User | A Program to Define a Python Class with Attributes and Methods to Demonstrate OOP | A Program to Use the Matplotlib Library to Plot a Graph based on the Given Data Points, and Enhancing the Graph with Labels and a Legend | A Program to Introduce the pandas Library by Creating a DataFrame from a Dictionary, and Performing Basic Data Manipulation Operations such as Sorting and Filtering | Basic Operations Using the Iris Data Set | Iris Data Loading and Visualization | Data Processing | Feature Selection | Classification Algorithm: SVM | Classification Algorithm: Decision Tree | Classification Algorithm: KNN | Classification Algorithm: Logistic Regression | Model Evaluation | Hyperparameter Tuning | Cross-validation | Ensemble Methods | Clustering Analysis | Deep Learning | Deep Learning: Keras
Appendix A: Model Course Structure
Appendix B: Model Question Papers
9788197424984 995.00
Machine learning
R 006.31 ROB(MAC)
Machine learning : a hands-on approach C R Rene Robin, Doreen Robin, Chandra Mouli P V S S R - Kolkata University press c2025 - xiv, 804p. P.B.
Chapter 1 Introduction to Machine Learning
Introduction | What is Machine Learning? | History of Machine Learning | Role of Machine Learning in Computer Science and Problem Solving | Why Machine Learning? | Adaptivity of Machine Learning | Designing versus Learning | Training versus Testing in Machine Learning | Machine Learning versus Automation | Predictive and Descriptive Tasks in Machine Learning | Some Terminology Related to Machine Learning | Types of Machine Learning | Passive Learning versus Active Learning | Online versus Batch Machine Learning | Differences between Machine Learning Models and Algorithms
| Disadvantages of Data-driven Solutions | Well-posed Machine Learning Problems | Designing a Learning System (Life Cycle of Machine Learning)
Chapter 2 Probability Theory and Statistics in Machine Learning
Introduction | Probability | Probability Theory | Joint, Marginal, and Conditional Probability | Statistics | Key Concepts of Probability Distributions | Probability Distributions | Examples of Probability Distributions | Conditional Distribution | Joint Distribution | Combinatorics | Probability Rules and Axioms | Moment Generating Function | Maximum Likelihood Estimation | Density Functions | Density Estimation | Challenges and Future Directions in Probability and Statistics for Machine Learning
Chapter 3 Linear Algebra
Introduction | Linear Regression | Matrix Decomposition | Vectors and Matrices | Eigenvalue and Eigenvectors | Norms and Vector Spaces | Optimization | Linear Transformation | Cramer’s Rule | Gaussian Elimination | LU Decomposition | QR Decomposition | Eigen Decomposition | Symmetric Matrices | Orthogonalization | Deep Learning with Linear Algebra
Chapter 4 Algorithms and Complex Optimizations
Sets, Relations, and Functions | Convex Sets and Convex Functions | Optimization Problems | Convex Optimization | Unconstrained Optimization | Constrained Optimization | Dual Optimization Problems | Dynamic Programming | Sublinear Algorithms | Graphs | Transforms | Information Theory | Manifolds
Chapter 5 Computational Learning Theory
Introduction | Objectives of Computational Learning Theory | History | Importance of Computational Learning Theory | The Main Methods | Probably Approximately Correct Learning | Complexity Theory of Machine Learning | Mistake-bound Learning Model | Instance-based learning | Lazy and Eager Learning | Generative Learning | Consistent Learning | Worst Case (Online) Learning | Applications of Computational Learning Theory | Evaluation Metrics for Computational Learning Theory | Future Directions in Computational Learning Theory
Chapter 6 Machine Learning Models
Introduction | Models in Machine Learning | Features | Concept Learning
Chapter 7 Unsupervised Learning
Introduction | What and Why of Unsupervised Learning | Types of Unsupervised Learning | Markov Models | Hidden Markov Model | Matrix Factorization and Matrix Completion Models | Generative Models | Latent Factor Models | Inference Models | Non-negative Matrix Factorization | Advantages of Unsupervised Learning | Disadvantages of Unsupervised Learning
Chapter 8 Supervised Learning: Classification
Introduction | K-Nearest Neighbor (KNN) | Decision Trees | Random Forests | Linear Classifiers | Applications of Supervised Learning | Limitations and Challenges of Supervised Learning
Chapter 9 Supervised Learning: Regression
Introduction | Linear Regression versus Non-Linear Regression | Types of Linear Models | Least Squares Method (LSM) | Multivariate Linear Regression | Nonlinearity and Kernel Methods | Generalized Linear Models | AdaBoost (Adaptive Boosting) | Regularized Regression | Backpropagation | Support Vector Regression | Decision Tree Regression | Random Forest Regression | Neural Network Regression |
Multi-layer Propagation | Radial Basis Functions | Splines | Curse of Dimensionality | Interpolations and Basis Functions | Multi-class/Structured Outputs, Ranking
Chapter 10 Artificial Neural Networks
Introduction to Neural Networks | Introduction to Artificial Neural Networks | Types of Artificial Neural Networks | Other Types of ANNs | Building Neural Network Architectures | Training Neural Networks with Backpropagation | Autoencoders | Applications of ANNs | Future of ANNs
Chapter 11 Trends in Machine Learning
Reinforcement Learning | Multitask Learning | Online Learning | Sequence Learning | Prediction Learning | Bagging and Boosting in Machine Learning | Trends in Machine Learning Technology
Chapter 12 Applications of Machine Learning in Various Industries
Real-World Problems Solved by Machine Learning | Applications of Machine Learning in the Retail Industry | Applications of Machine Learning in the Logistics Industry | Applications of Machine Learning in the Manufacturing Industry | Applications of Machine Learning in the Energy and Utilities Industry | Applications of Machine Learning in the Travel Industry | Applications of Machine Learning in the Banking Industry | Applications of Machine Learning in the Finance Industry | Applications of Machine Learning in the Insurance Industry
Chapter 13 Machine Learning Programming: Capstone Projects Using Python and R
Introduction | Installing Python | The sklearn Package | Anaconda Navigator | Data Operations on the Iris Data Set | Finding Outliers | Removing Outliers | Imputing Null Values | Capping the Outlier Values | Splitting the Data into Training and Testing Data | Training and Evaluating the Model | Regularization Techniques that Prevent Overfitting | Implement Linear Regression | Implement Logistic Regression | Decision Tree Classifier | Implement SVM | Implement PCA | Implement Steepest Descent | Implement Random Forest | Implement Random Search | Implement Naïve Bayes | Implement Single-Layer Perceptron Learning Algorithm | Implement Radial Basis Functions | Implement Linear Classifier | Implement Bayesian Classifier | Implement K-Nearest Neighbor Classifier | Implement Linear Discriminant Analysis | Implement Locality Preserving Projection | Implement Logic Gates without Perceptron Model | Implement Logic Gates with Perceptron Model | Handwritten Classification using CNN | Introduction to R Programming
Chapter 14 Machine Learning Programming Using Jupyter Notebook
Introduction | Using the Online Interface of Jupyter Notebook | A Python Program that Demonstrates the Use of Data Types | A Python Program that Asks for User Input and Uses Conditional Statements to Respond with Different Outputs | A Python Program that Prints Out a Sequence of Numbers using a for Loop and then Asks the User to Do the Same with a while Loop | A Python Program that Defines a Function to Calculate the Area of a Circle, Given its Radius, and then Calls that Function with Different Values | A Python Program that Creates a List of Items and a Dictionary of Key–Value Pairs, and then Demonstrates How to Access and Modify Elements | A Python Program that Reads a Text File, Counts the Number of Words, and Writes the Result to a New File | A Python Program that Intentionally Raises an Error and then Catches it with a try-except Block, Printing an Informative Message to the User | A Program to Define a Python Class with Attributes and Methods to Demonstrate OOP | A Program to Use the Matplotlib Library to Plot a Graph based on the Given Data Points, and Enhancing the Graph with Labels and a Legend | A Program to Introduce the pandas Library by Creating a DataFrame from a Dictionary, and Performing Basic Data Manipulation Operations such as Sorting and Filtering | Basic Operations Using the Iris Data Set | Iris Data Loading and Visualization | Data Processing | Feature Selection | Classification Algorithm: SVM | Classification Algorithm: Decision Tree | Classification Algorithm: KNN | Classification Algorithm: Logistic Regression | Model Evaluation | Hyperparameter Tuning | Cross-validation | Ensemble Methods | Clustering Analysis | Deep Learning | Deep Learning: Keras
Appendix A: Model Course Structure
Appendix B: Model Question Papers
9788197424984 995.00
Machine learning
R 006.31 ROB(MAC)