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    Foundations to Frontiers in Machine Learning
    Foundations to Frontiers in Machine Learning

    Foundations to Frontiers in Machine Learning

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    As Per NEP w.e.f. 2024-25

    M.Sc. (Computer Science)

    Semesters-III

    Book ID: 2116

    Author: Prof. (Dr.) Chitra Desai

    ISBN: 978-93-94022-60-7

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    Contents

    1. Introduction to Machine Learning
    1. Introduction
    2. Data Science
    3. Artificial Intelligence
    4. Machine Learning: A Subset of Artificial Intelligence
    4.1 Getting Started with Machine Learning: Key Concepts and Terminology
    4.2 Significance of Artificial Intelligence and Machine Learning
    4.3 Types of Machine Learning
    4.4 Dimensionality Reduction
    4.5 Machine Learning Life Cycle
    2. Regression in Machine Learning
    1. Introduction
    2. Regression
    3. Terminologies Related to Regression Analysis in Machine Learning
    4. Linear Regression
    4.1 Simple Linear Regression
    4.2 Multiple Linear Regression
    5. Residual Analysis
    6. Covariance
    7. Multiple Variables in Regression
    8. Non-Normality and Heteroscedasticity
    9. Hypothesis Testing in Regression Models
    10. Confidence Intervals of Slope in Regression Analysis
    11. R-square and Goodness of Fit in Regression Analysis
    12. Influential Observations and Leverage in Regression Analysis
    13. Regularization Methods in Regression Analysis
    14. Categorical Variables in Regression
    15. Support Vector Regression and Decision Tree Regression
    3. Classification in Machine Learning
    1. Introduction
    2. Logistic Regression
    2.1 Comparison with Linear Regression
    2.2 Components of Logistic Regression
    2.3 Multiclass Logistic Regression Using Logistic Regression
    2.4 Threshold in Logistic Regression
    2.5 Advantages of Logistic Regression
    2.6 Limitations of Logistic Regression
    3. Support Vector Machine
    3.1 Working of SVM
    3.2 Linear Modelling Using SVM
    3.3 Grid Search
    4. Non-linear Models for Classification
    5. Non-Linear Model Using Support Vector Machines for Classification
    5.1 Polynomial Kernel
    5.2 Sigmoid Kernel
    6. K-Nearest Neighbors (KNN)
    6.1 Working of KNN
    6.2 Computational Geometry in KNN: Voronoi Diagrams and Delaunay Triangulations
    6.3 Wilson Editing and its Role in Improving KNN Efficiency
    7. Naïve Bayes
    7.1 Probability Estimation Techniques
    7.2 M-Estimates in Naïve Bayes
    7.3 Data Preprocessing Requirements for Effective Modeling
    8. Naïve Bayes vs. K-Nearest Neighbors (KNN)
    4. Model Evaluation
    1. Introduction
    2. Regression Metrics
    3. Classification Metrics
    5. Reinforcement Learning
    1. Introduction
    2. Distinction between Reinforcement Learning and Other Learning Paradigms
    3. Markov Decision Processes (MDP): A Framework for RL
    4. Exploration vs. Exploitation Dilemma
    5. Strategies to Address the Dilemma
    6. Q-Learning
    6. Unsupervised Learning Model
    1. Introduction
    2. Clustering
    2.1 Distance Measures in Clustering
    2.2 Different Clustering Methods
    2.3 Selecting the Optimal Number of Clusters
    3. Association Rule Mining
    3.1 Algorithms for Association Rule Mining
    4. Recommendation Engine
    5. Anomaly Detection
    6.  Dimensionality Reduction
    6.1 Principal Component Analysis
    6.2 Singular Value Decomposition (SVD)
    7. Advantages and Disadvantages of Unsupervised Learning
    7. Time Series Data
    1. Introduction
    2. Components and Factors for Analysing Time Series Data
    3. Decomposition in Time Series    
    4. Stationarity
    4.1 Types of Stationarity
    4.2 Test for Stationarity
    4.3 Bias and Stationarity
    5. Appropriate Contexts for Time Series Analysis: Recognizing Its Limitations
    6. Forecasting
    6.1 Moving Averages (MA)
    6.2 Exponential Smoothing
    6.3 ARIMA (AutoRegressive Integrated Moving Average)
    6.4 State Space Models
    6.5 Machine Learning Models
    7. Setting up Machine Learning Problem for Time Series Data
    8. Case Study
    8. Neural Networks
    1. Introduction
    2. Human Brian and Artificial Neural Network
    3. Types of Neural Networks
    4. Data Representation in Neural Network
    5. Basic Architecture of ANN
    6. Loss Function
    7. Gradient Descent and Chain Rule
    7.1 Gradient Descent
    7.2 Chain Rule
    8. Learning Algorithm: Backpropagation
    9. Regularization
    9.1 Types of Regularization
    9.2 Implementation of Regularization in Neural Networks
    10. Optimization Techniques
    10.1 Gradient Descent
    10.2 Challenges in Optimization
    10.3 Stochastic Gradient Descent (SGD)
    10.4 Momentum
    10.5 Nesterov Accelerated Gradient
    11. Adaptive Learning Techniques
    11.1 AdaGrad (Adaptive Gradient Algorithm)
    11.2 RMSprop (Root Mean Square Propagation)
    11.3 Adam (Adaptive Moment Estimation)
    9. Convolutional Neural Networks
    1. Introduction
    2. Overview of CNN Workflow
    3. ANN vs CNN
    4. Components of CNN
    5. Convolutional Layer
    6. Stride
    7. Pooling
    8. Flattening Layer
    9. Fully Connected Layer
    10. Case Study
    10. Tree Models and Ensemble Learning
    1. Introduction
    2. Decision Trees
    2.1 Decision Trees Vs Other Algorithms   
    2.2 Tree Concepts
    2.3 Decision Tree Splitting Criteria
    2.4 Decision Tree Algorithms
    2.5 Working of Decision Trees
    3. Model Combination Schemes in Ensemble Learning
    4. Bagging - Random Forest
    4.1 Working of Random Forest
    4.2 Feature Importance Using Random Forest
    5. Voting
    6. Stacking
    7. Error-Correcting Output Codes
    8. Gaussian Mixture Model
    9. The Expectation-Maximization (EM) Algorithm
    10. Ensemble Learning in Deep Learning
    Appendix
    M.Sc.
    2116
    10 Items

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    Foundations to Frontiers in Machine Learning
    Vision Publications, Educational Book, SPPU, unipune

    Foundations to Frontiers in Machine Learning

    ₹405.00
    ₹450.00
    Save 10%