St. Xavier's University Library Search for 'su:"MACHINE LEARNING"'

Machine Learning

]]> By Dutt, Saikat .
Noida Pearson 2019 .
xxvii, 428 9789353066697

Practical time series analysis : prediction with statistics and machine learning

]]> By Nielsen, Aileen .
Kolkata Shroff publishers & distributors 2020 .
xvi, 480 , Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1. Time Series: An Overview and a Quick History. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 The History of Time Series in Diverse Applications 2 Medicine as a Time Series Problem 2 Forecasting Weather 6 Forecasting Economic Growth 7 Astronomy 9 Time Series Analysis Takes Off 10 The Origins of Statistical Time Series Analysis 12 The Origins of Machine Learning Time Series Analysis 13 More Resources 13 2. Finding and Wrangling Time Series Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Where to Find Time Series Data 18 Prepared Data Sets 18 Found Time Series 25 Retrofitting a Time Series Data Collection from a Collection of Tables 26 A Worked Example: Assembling a Time Series Data Collection 27 Constructing a Found Time Series 33 Timestamping Troubles 35 Whose Timestamp? 35 Guesstimating Timestamps to Make Sense of Data 36 What’s a Meaningful Time Scale? 39 Cleaning Your Data 40 Handling Missing Data 40 Upsampling and Downsampling 52 Smoothing Data 55 iiiSeasonal Data 60 Time Zones 63 Preventing Lookahead 67 More Resources 69 3. Exploratory Data Analysis for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Familiar Methods 73 Plotting 74 Histograms 77 Scatter Plots 78 Time Series–Specific Exploratory Methods 81 Understanding Stationarity 82 Applying Window Functions 86 Understanding and Identifying Self-Correlation 91 Spurious Correlations 102 Some Useful Visualizations 104 1D Visualizations 104 2D Visualizations 105 3D Visualizations 113 More Resources 117 4. Simulating Time Series Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 What’s Special About Simulating Time Series? 120 Simulation Versus Forecasting 121 Simulations in Code 121 Doing the Work Yourself 122 Building a Simulation Universe That Runs Itself 128 A Physics Simulation 134 Final Notes on Simulations 140 Statistical Simulations 141 Deep Learning Simulations 141 More Resources 142 5. Storing Temporal Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Defining Requirements 145 Live Data Versus Stored Data 146 Database Solutions 148 SQL Versus NoSQL 149 Popular Time Series Database and File Solutions 152 File Solutions 157 NumPy 158 Pandas 158 iv | Table of ContentsStandard R Equivalents 158 Xarray 159 More Resources 160 6. Statistical Models for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Why Not Use a Linear Regression? 163 Statistical Methods Developed for Time Series 166 Autoregressive Models 166 Moving Average Models 181 Autoregressive Integrated Moving Average Models 186 Vector Autoregression 196 Variations on Statistical Models 201 Advantages and Disadvantages of Statistical Methods for Time Series 203 More Resources 204 7. State Space Models for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 State Space Models: Pluses and Minuses 209 The Kalman Filter 210 Overview 210 Code for the Kalman Filter 212 Hidden Markov Models 218 How the Model Works 218 How We Fit the Model 220 Fitting an HMM in Code 224 Bayesian Structural Time Series 229 Code for bsts 230 More Resources 235 8. Generating and Selecting Features for a Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Introductory Example 240 General Considerations When Computing Features 241 The Nature of the Time Series 242 Domain Knowledge 242 External Considerations 243 A Catalog of Places to Find Features for Inspiration 243 Open Source Time Series Feature Generation Libraries 244 Domain-Specific Feature Examples 249 How to Select Features Once You Have Generated Them 252 Concluding Thoughts 255 More Resources 256 Table of Contents | v9. Machine Learning for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Time Series Classification 260 Selecting and Generating Features 260 Decision Tree Methods 264 Clustering 272 Generating Features from the Data 273 Temporally Aware Distance Metrics 280 Clustering Code 285 More Resources 287 10. Deep Learning for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Deep Learning Concepts 292 Programming a Neural Network 294 Data, Symbols, Operations, Layers, and Graphs 294 Building a Training Pipeline 298 Inspecting Our Data Set 299 Steps of a Training Pipeline 302 Feed Forward Networks 318 A Simple Example 318 Using an Attention Mechanism to Make Feed Forward Networks More Time-Aware 321 CNNs 324 A Simple Convolutional Model 325 Alternative Convolutional Models 327 RNNs 330 Continuing Our Electric Example 332 The Autoencoder Innovation 334 Combination Architectures 335 Summing Up 340 More Resources 341 11. Measuring Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 The Basics: How to Test Forecasts 344 Model-Specific Considerations for Backtesting 347 When Is Your Forecast Good Enough? 348 Estimating Uncertainty in Your Model with a Simulation 350 Predicting Multiple Steps Ahead 353 Fit Directly to the Horizon of Interest 353 Recursive Approach to Distant Temporal Horizons 354 Multitask Learning Applied to Time Series 354 Model Validation Gotchas 355 More Resources 355 vi | Table of Contents12. Performance Considerations in Fitting and Serving Time Series Models. . . . . . . . . . . . 357 Working with Tools Built for More General Use Cases 358 Models Built for Cross-Sectional Data Don’t “Share” Data Across Samples 358 Models That Don’t Precompute Create Unnecessary Lag Between Measuring Data and Making a Forecast 360 Data Storage Formats: Pluses and Minuses 361 Store Your Data in a Binary Format 361 Preprocess Your Data in a Way That Allows You to “Slide” Over It 362 Modifying Your Analysis to Suit Performance Considerations 362 Using All Your Data Is Not Necessarily Better 363 Complicated Models Don’t Always Do Better Enough 363 A Brief Mention of Alternative High-Performance Tools 364 More Resources 365 13. Healthcare Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Predicting the Flu 367 A Case Study of Flu in One Metropolitan Area 367 What Is State of the Art in Flu Forecasting? 383 Predicting Blood Glucose Levels 384 Data Cleaning and Exploration 385 Generating Features 390 Fitting a Model 396 More Resources 401 14. Financial Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Obtaining and Exploring Financial Data 404 Preprocessing Financial Data for Deep Learning 410 Adding Quantities of Interest to Our Raw Values 410 Scaling Quantities of Interest Without a Lookahead 411 Formatting Our Data for a Neural Network 413 Building and Training an RNN 416 More Resources 423 15. Time Series for Government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Obtaining Governmental Data 426 Exploring Big Time Series Data 428 Upsample and Aggregate the Data as We Iterate Through It 431 Sort the Data 432 Online Statistical Analysis of Time Series Data 436 Remaining Questions 446 Further Improvements 446 More Resources 447 Table of Contents | vii16. Time Series Packages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Forecasting at Scale 449 Google’s Industrial In-house Forecasting 450 Facebook’s Open Source Prophet Package 452 Anomaly Detection 457 Twitter’s Open Source AnomalyDetection Package 457 Other Time Series Packages 460 More Resources 461 17. Forecasts About Forecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Forecasting as a Service 463 Deep Learning Enhances Probabilistic Possibilities 464 Increasing Importance of Machine Learning Rather Than Statistics 465 Increasing Combination of Statistical and Machine Learning Methodologies 466 More Forecasts for Everyday Life 466 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 9789352139255

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Deep learning /

]]> By Goodfellow, Ian,.
Massachusetts Cambridge 2016 .
xxii, 775 pages : , An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. 24 cm..
9780262035613 (hardcover : alk. paper) | 0262035618 (hardcover : alk. paper)

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Deep learning with Python

]]> By Chollet, Francois .
Shelter Island Manning 2021 .
xxiv, 478 9781617296864

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Deep Learning : A practitioner's approach

]]> By Patterson, Josh .
New Delhi Shroff publishers & distributors 2021 .
xxi, 507 , Includes index & appendix 9789352136049

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Pattern recognition and machine learning

]]> By Bishop, Christopher M..
New York Springer 2009 .
738p. , includes index 9781493938438

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Machine learning using R

]]> By Kumar Rahul .
New Delhi Wiley 2022 .
xxii, 430 , Includes index 9789354246111

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The elements of statistical learning : data mining, inference, and prediction /

]]> By Hastie, Trevor..
New York, NY : Springer, 2009 .
xxii, 745 p. : , Introduction Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 1-8 Overview of Supervised Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 9-41 Linear Methods for Regression Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 43-99 Linear Methods for Classification Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 101-137 Basis Expansions and Regularization Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 139-189 Kernel Smoothing Methods Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 191-218 Model Assessment and Selection Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 219-259 Model Inference and Averaging Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 261-294 Additive Models, Trees, and Related Methods Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 295-336 Boosting and Additive Trees Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 337-387 Neural Networks Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 389-416 Support Vector Machines and Flexible Discriminants Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 417-458 Prototype Methods and Nearest-Neighbors Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 459-483 Unsupervised Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 485-585 Random Forests Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 587-604 Ensemble Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 605-624 Undirected Graphical Models Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 625-648 High-Dimensional Problems: p N Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 649-698 Back Matter 25 cm..
9780387848570 (hardcover : alk. paper) | 9780387848587 (electronic)

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Machine Learning

]]> By Dutt, Saikat .
Noida Pearson 2019 .
xxvii, 428 9789353066697

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Statistical Sherlock: Sleuthing out fake news stories

By Sen, Ritobrata..
Kolkata St. Xavier's University 2024 .
76p. , Includes graph

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Practical machine learning for computer vision : end-to end machine learning for images

]]> By Lakshmanan, Valliappa .
Kolkata Shroff publishers & distributors 2021 .
xvi, 463 , Includes index 9789391043834

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Apply data science : introduction, applications and projects

]]> Wiesbaden Springer 2023 .
232p. , includes index 9783658387976

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Introduction to Machine Learning with R : Rigorous Mathematical analysis

]]> By Burger, Scott V. .
Kolkata Shroff publishers 2018 .
ix, 212 , Table of contents : What is a model? -- Supervised and unsupervised machine learning -- Sampling statistics and model training in R -- Regression in a nutshell -- Neural networks in a nutshell -- Tree-based methods -- Other advanced methods -- Machine learning with the caret package -- Encyclopedia of machine learning models in caret. Includes index 9789352137251

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Reinforcement Learning : industrial applications of intelligent agents

]]> By Winder, Phil.
Kolkata Shroff 2021 .
xxiii, 379 , Includes index, glossary & appendix 9789385889509

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Probabilistic Machine learning for Finance and investing : A primer to generative AI with Python

]]> By Kanungo, Deepak K. .
Kolkata Shroff 2023 .
xv, 247 , Includes index 9789355429995

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Machine Learning and Artificial Intelligence

]]> By Joshi, Ameet V. .
Switzerland Springer 2023 .
xxi, 271 p. 24 cm..
9783031122811

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Machine learning : a hands-on approach

]]> By Robin, C R Rene.
Kolkata University press 2025 .
804p. , includes index 9788197424984

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Synthetic media : navigating the futur of ai and ml generated content, opportunities, threats and the future of humanity

]]> Kolkata Mitra 2025 .
462p. 9789334224290

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Data science and machine learning with r

]]> By Thareja, Reema.
Chennai McGraw Hill 2025 .
472p. , Table of contents Chapters Chapter 1: Introduction to Data Sciences and Machine Learning Chapter 2: Machine Learning Algorithms Chapter 3: Machine Learning Algorithms - II Chapter 4: Introduction to R Chapter 5: More on Data Structures Chapter 6: Decision Control and Looping Statements Chapter 7: Generating and Manipulating Data in R Chapter 8: Working with Data Chapter 9: Using dplyr () and tidyr () packages Chapter 10: Plotting graphs in R Chapter 11: Social Media Mining Chapter 12: Implementing Machine Learning Algorithms Chapter 13: Implementing Machine Learning Algorithms - II Index Online Content Appendices Case Studies includes index 9789354600333

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Machine learning principles and techniques

]]> Kolkata Aryan 2025 .
524p. , includes index 9788198761934

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Computational statistics and machine learning : as per the latest curriculum of the directives of NEP 2020

]]> By Pal, Shilpi.
New Delhi Global net 2025 .
356p. 9789367615560

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Machine learning principles and techniques

]]> Kolkata Aryan 2025 .
524p. , includes index 9788198761934

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Machine learning : theory and practice

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Machine learning : a hands-on approach

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Machine learning : aicte recommended text book

]]> By Chopra, Rajiv.
New Delhi Khanna 2025 .
xii, 348p. , includes index 9789386173423

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Mathematics for Machine Learning

]]> By Deisenroth, Marc Peter .
New Delhi Cambridge University Press 2021 .
xvii, 371 , Includes index & reference 9781009108850

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Deep learning : algorithms and applications

]]> By Ramalakshmi, R..
Delhi Cengage 2026 .
various pages , Part I Foundations 1. Introduction to Deep Learning 2. Machine Learning Fundamentals 3. Mathematical Building Blocks Part II Deep Learning Models 4. Artificial Neural Network (ANN) 5. Convolutional Neural Network (CNN) 6. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) 7. Gated Recurrent Unit (GRU) and Generative Adversarial Networks (GANs) 8. Optimization Algorithms and Regularization Techniques Part III Advanced Techniques in Deep Learning 9. Auto Encoders, Attention Mechanisms, and Transformers 10. Reinforcement Learning (RL) and Deep Q-Network (DQN) 11. Neural Architecture Search (NAS) and Automated Machine Learning (AutoML) Part IV Deep Learning Frameworks and Tools 12. Popular Deep Learning Frameworks and Libraries Part V Ethical and Social Implications of Deep Learning 13. Bias, Fairness, Data Protection, and Ethical Challenges in DL Models Part VI Deep Learning Applications and Future Trends 14. DL Applications in Computer Vision, NLP, Recommender Systems, and Time-series 15. Challenges, Opportunities and Future Research Trends 9789366606606

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