• V. Sasikumar et al., A Course in Listening and Speaking I, 2nd Edition, Cambridge University Press, 2006.
• Raymond Murphy, English Grammar in Use, 5th Edition, Cambridge University Press, 2019.
• Alice Oshima and Ann Hogue, Introduction to Academic Writing, 3rd Edition, Pearson Longman, 2007.

• Oxford Advanced Learner's Dictionary of English, 9th Edition, 2016.
• John and Liz Soars, New Headway, 3rd Edition, Oxford University Press, 2007.

• Wayne Winston, Microsoft Excel Data Analysis and Business Modeling, 7th Edition, Pearson Education, Inc., 2022.
• Michael Alexander, Richard Kusleika, John Walkenbach, Excel 2019 Bible, 1st Edition, Wiley, 2018.

• Gordon S. Linoff, Data Analysis Using SQL and Excel, 2nd Edition, Wiley Publishing Inc., 2016.

• Hogg, R.V., McKean, J. and Craig, A.T., Introduction to Mathematical Statistics, 7th Edition, Pearson Education, 2012.
• Rice, J.A., Mathematical Statistics and Data Analysis, 3rd Edition, Cengage Learning, 2006.

• Rohatgi, V.K. and Saleh, A.M.E., An Introduction to Probability and Statistics, 3rd Edition, John Wiley & Sons, 2015.

• Kernighan B.W and Dennis M. Ritchie, The C Programming Language, 2nd Edition, Pearson Education India, 2015.
• Byron S. Gottfried, Programming with C, 4th Edition, McGraw Hill (Schaum's Outlines), 2018.

• David Poole, Linear Algebra: A Modern Introduction, 4th Edition, Cengage Learning, 2014.
• Seymour Lipschutz and Marc Lars Lipson, Linear Algebra, 4th Edition, McGraw Hill (Schaum’s Outline Series), 2009.

• Rafael A. Irizarry, Introduction to Data Science: Data Analysis and Prediction Algorithms with R, 1st Edition, CRC Press, 2019.
• Hui Lin and Ming Li, Practitioner’s Guide to Data Science, 1st Edition, Chapman & Hall/CRC, 2023.

• Joan Lambert and Steve Lambert, Windows 10 Step by Step, Microsoft Press, 2015.
• Mark Sobell and Matthew Helmke, A Practical Guide to Linux Commands, Editors, and Shell Programming, 4th Edition, Addison-Wesley, 2017.

• Randal E. Bryant and David R. O’Hallaron, Computer Systems: A Programmer’s Perspective, 3rd Edition, Pearson Education Limited, 2016.

• Charles Dierbach, Introduction to Computer Science using Python: A Computational Problem-Solving Focus, 1st Edition, Wiley, 2015.
• Mark Lutz, Learning Python, 5th Edition, O’Reilly Media, Inc., 2013.

• Wes McKinney, Python for Data Analysis, O’Reilly Media, Inc., 2012.

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2020.
• R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018.

• Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser, Data Structures and Algorithms in Python, John Wiley & Sons, 2013.
• Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 4th Edition, Pearson Education, Inc., 2014.

• T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Prentice-Hall of India, 2009.
• A. V. Aho, J. E. Hopcroft, and J. D. Ullman, The Design and Analysis of Computer Algorithms, Pearson Education, 2006.

• Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoit Liquet, The R Software: Fundamentals of Programming and Statistical Analysis, Springer, 2013.
• Alain F. Zuur, Elena N. Ieno, Erik H.W.G. Meesters, A Beginner's Guide to R (Use R), Springer, 2009.
• Christian Heumann, Michael Schomaker, Shalabh, Introduction to Statistics and Data Analysis with Exercises, Solutions and Applications in R, Springer, 2016.

Relational DBMS: Entity relationship (ER) Model, relational algebras; SQL queries, constraints, triggers; SQL and front-end tools; Storage and file structure: Overview of secondary storage, RAID and flash storage, indexing (tree, hash, and bitmap), implementation of relational operators; Transaction management: ACID properties, concurrency control, crash recovery.

Introduction to Databases: Concepts, importance, and types; Relational Data Model: Fundamentals, data integrity, normalization; Entity-Relationship (ER) Model; Storage and File structure; Structured Query Language (SQL); Database Design; Database Administration; Transaction management; Database Connectivity and Application Development; NoSQL Databases

Practical Component: Assignments based on theory.

• R. Ramakrishnan, J. Geherke, Database Management Systems, Third Edition, McGraw Hill, 2014.
• Elmasri Ramez and Navathe Shamkant, Fundamentals of Database System, Seventh Edition, Pearson Education, 2017.

• H. Garcia-Molina, J. Ullman, J. Widom, Database System: The Complete Book, Second Edition, Pearson, 2013.
• P. Bailis, J. Hellerstein, M. Stonebraker, Readings in Database Systems, Fifth Edition, 2015.

Introduction: Overview of Java programming language, Setting up Java Development Environment, Basic syntax and structure of Java programs; Data Types and Variables; Type conversion and casting; Control Flow Statements; Looping statements; Methods and Functions, Object-Oriented Programming (OOP) Concepts; Exception handling; Collections framework; File handling; Data Structures: Arrays and multidimensional arrays, Linked lists, stacks, and queues, Trees and graphs; GUI Programming; Database Connectivity; Multithreading; Networking; Data Science and AI Libraries.

• Herbert Schildt, Java: The Complete Reference, Eleventh Edition, McGraw-Hill Education, 2019.

• David Kopec, Classic Computer Science Problems in Java, First Edition, Manning, 2021.

Introduction: Optimization problems and existence of optimal solutions, convex sets and convex functions; Unconstrained optimization: Basic properties of solutions and algorithms, gradient method, Newton’s method, quasi-Newton method; Linear optimization: Simplex algorithm, duality; Constrained optimization: Equality and inequality constraints, projected gradient method; Convex optimization.

• Edwin K. P. Chong & Stanislaw H. Zak, An Introduction To Optimization, Fourth Edition, Wiley India, 2017.

• S. Boyd and L. Vandenberghe, Convex Optimization, First Edition, Cambridge India, 2004.

Brief review of random variables: expectation, variance, covariance, estimation and inference in the context of economics; Classical Linear Regression Model: least squares estimation, unbiasedness and efficiency, Gauss-Markov theorem, hypothesis testing, goodness of fit; Nonlinear models, Dummy variables; Heteroscadasticity, Autocorrelation and Multicollinearity: detection, implications and possible remedies; Omitted variable, measurement errors and instrumental variables; Binary response model, Sample selection problem.

• Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, Seventh Edition, South-Western College Publishing, 2020.

• Russell Davidson and James Mackinnon, Econometric Theory and Methods, Revised Edition, Oxford University Press, 2009.

Introduction: Definitions, Review of basics of data analysis, applications; Data Warehousing: Definition, architectures, dimensional modeling and star schema, ETL (extract, transform, load) processes; Finding Similar Items: Similarity measures and distance metrics, Shingling and locality-sensitive hashing; Frequent Pattern Mining: Itemset, substring, sequence, pattern evaluation and interestingness measures; Graph Mining: Graph data and representations, link analysis, pattern mining, graph clustering techniques; Mining Data Streams: characteristics of data streams, sliding window models, approximate and sketching techniques, change detection and concept drift.

• Han, Jiawei, Jian Pei, and Hanghang Tong, Data mining: Concepts and Techniques, 4th Edition, Morgan Kaufmann, 2022.
• Robinson, Ian, Jim Webber, and Emil Eifrem, Graph Databases: New Opportunities for Connected Data, 2nd Edition, O'Reilly Media, Inc., 2015.
• Ponniah, Paulraj, Data Warehousing Fundamentals for IT Professionals, 2nd Edition, John Wiley & Sons, 2012.

• Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, 3rd Edition, Cambridge University Press, 2020.
• Kimball, Ralph, and Margy Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Wiley, 2013.
• Reis, Joe, and Matt Housley, Fundamentals of Data Engineering, O'Reilly Media, Inc., 2022.

Principles of point estimation; Properties of estimators: unbiasedness, consistency, sufficiency, mean squared errors; Methods of estimation: least squares estimation, method of moments estimators, maximum likelihood estimators (MLEs), statistical properties of MLEs, Fisher information, Cramer Rao Lower Bound; Confidence intervals, Bootstrap percentile method; Testing of hypothesis: Binary hypothesis testing, Type-I and type-II errors, power function, likelihood ratio tests, Neyman-Pearson lemma; Significance testing: general approach, generalized likelihood ratio tests; Bayesian vs Classical statistics, Bayesian inference and posterior distribution, Maximum a Posteriori probability rule, Bayesian least means squares estimation.

• Wasserman, L., All of Statistics: A concise course in statistical inference, First Edition, Springer, New York, 2004.
• Pishro-Nik, Hossein, Introduction to Probability, Statistics, and Random processes, First Edition, Kappa Research, LLC, 2014.

• Dimitri P. Bertsekas, John N. Tsitsiklis, Introduction To Probability, Second Edition, Athena Scientific, 2008.

Introduction to Signals: continuous-time, discrete-time signals, properties of signals and various signal operations; Signal representation: signal space and orthogonal bases, Fourier series representation of continuous-time signals, Fourier transform (FT) and its properties; Sampling theorem: aliasing, signal reconstruction, ideal interpolation; Discrete-time FT, Discrete Fourier Transform (DFT) and its properties; System classification and properties; Linear Time Invariant (LTI) systems; Time-domain representation of LTI systems: Impulse response, convolution and response to arbitrary input; Frequency response of LTI systems.

• B. P. Lathi, Principles of Linear Systems and Signals, Second Edition, Oxford, 2009.
• H. Hsu and R. Rajan, Signals and Systems, Second Edition, Schaum Series, 2017.

Fundamentals: Understanding social media contents, frameworks, characteristics, challenges; Mathematical foundations; Data Crawling: Techniques, policies, ethics, responsibilities; Different APIs of crawling data. Text content analysis: Tokenizer, lemmatization; Heaps law, Zipf law; retrieval models, relevance, ranking; Text Embedding; Feature selection, text classification; Network analytics: fundamentals, centrality, link prediction, community detection; network embedding; Applications: Real world applications of social media data.

• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008.
• Subodha Kumar, Liangfei Qiu, Social Media Analytics and Practical Applications - The Change to the Competition Landscape, First Edition, CRC Press, 2021.

Understanding Structure of Data: Entities, attributes, relationships, data types, data models basics and types, dataset creation and best practices, data preparation for visualization; Introduction to Visual Perception; Guidelines for data visualization; Coordinate system and color scales; Visualizing Amounts; Visualizing Distributions; Visualizing proportions; Visualizing Associations Among Variables; Visualizing Time Series and trends; Visualizing geospatial data and Uncertainty: Projections; Principles of Figure Design; Advanced Topics: Image file formats, visualization softwares, and storytelling using data.

• Foxwell, Harry J., Creating Good Data: A Guide to Dataset Structure and Data Representation, First Edition, Apress, Berkeley, 2020.
• Claus O. Wilke, Fundamentals of Data Visualization, First Edition, Shroff/O'Reilly, 2019.
• Jonathan Schwabish, Better Data: A Guide for Scholars, Researchers, and Wonks, First Edition, Columbia University Press, 2021.

Introduction to time series data: Practical examples, Trend in time series data, Parametric trend, LS estimation, Differencing, Nonparametric methods, Trend and noise, Simple time series models, Analysis objectives; Stationary processes: basic properties, linear processes, ARMA(1,1) process, properties, forecasting stationary time series; ARMA(p,q) models: processes, ACF and PACF, spectral densities, periodogram, time-invariant linear filters, spectral density of an ARMA(p,q) process; Modeling and Forecasting with ARMA(p,q) processes: Yule-Walker estimation, Burg’s algorithm, Innovations algorithm, Maximum Likelihood Estimation, Diagnostic checking, Forecasting, Order selection criterion; Nonstationary and seasonal models; Applications and recent developments.

• Brockwell, Peter J., and Richard A. Davis, Introduction to Time Series and Forecasting, Third Edition, Springer New York, 2002.
• Hyndman, Rob J., and George Athanasopoulos, Forecasting: Principles and Practice, Third Edition, OTexts, 2021.

• Kay, Steven M., Fundamentals of Statistical Signal Processing: Estimation Theory, First Edition, Pearson, 1993.
• Nielsen, Aileen, Practical Time Series Analysis: Prediction with Statistics and Machine Learning, First Edition, O'Reilly Media, 2019.

Introduction: Supervised and unsupervised learning, Generative and discriminative models, Concept of dimensionality and feature vectors, Multi-dimensional Gaussian distribution, Mean vector and covariance matrix in Gaussian distribution; Supervised Learning: Bayesian classification principles, Computation of decision surfaces, Error calculation and performance measures, Risk minimization strategies, Zero-one loss function, Maximum Likelihood Estimation and Maximum A Posteriori Estimation, Bayesian learning concepts, Parzen windows and k-nearest neighbor algorithm, Distance measures and Dynamic Time Warping, Decision trees for classification tasks; Unsupervised Learning: K-means clustering, Hierarchical Agglomerative Clustering, Gaussian Mixture Models, Density-Based Spatial Clustering of Applications with Noise; Dimensionality Reduction Techniques: Curse of dimensionality, Applications of Principal Component Analysis and Fisher Discriminant Analysis to classification problems.

• E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India), 2015.
• R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, Second Edition, John Wiley & Sons, 2006.

• C. M. Bishop, Pattern Recognition and Machine Learning, Second Edition, Springer, 2011.
• S. O. Haykin, Neural Networks and Learning Machines, Third Edition, Pearson Education, 2016.

Introduction to Recommender Systems; Traditional Recommendation Techniques: Nearest neighbor-based, associative rule-based, content-based filtering, collaborative filtering; Matrix Factorization Techniques: Introduction, SVD review, alternating least squares, non-negative matrix factorization; Advanced Recommendation Techniques: Context-aware, hybrid, model-based methods; Evaluation metrics and methodologies; Recommender System Challenges: Cold start problem, data sparsity, scalability, privacy, and explainability; Case Studies and Applications: E-commerce, social media, multimedia, and other domains; Ethical Considerations in recommendation systems.

• Charu C. Aggarwal, Recommender Systems: The Textbook, First Edition, Springer, 2016.
• Jannach D., Zanker M., and Fel Fering A., Recommender Systems: An Introduction, First Edition, Cambridge University Press, 2011.

• To be updated as required.

Fundamentals: Computer network basics, concepts of distributed systems, overview of cloud computing; Cloud architecture: Service oriented architecture, event driven architecture, cloud software as a service, cloud platform as a service, cloud infrastructure as a service; Cloud computing security and architecture: Confidentiality, integrity, availability, trusted cloud computing, secure execution environments and communications, micro architectures, identity management and access control, autonomic security; Risks in cloud computing: CIA triad, privacy and compliance risks, threats to infrastructure, data and access control; Virtual machines: Virtualization, resource allocation; Cloud storage: AWS, Azure, Google cloud; Cloud computing applications: Machine learning, big data analytics;

• N. B. Ruparelia, Cloud Computing, MIT Press, 2023.
• T. Erl and E. B. Monroy, Cloud Computing: Concepts, Technology, Security & Architecture, Pearson, 2nd Edition, 2024.

• A. T. Velte, T. J. Velte, and R. Elsenpeter, Cloud Computing: A Practical Approach, Tata McGraw-Hill, New Delhi, 2010.

Introduction: Engineered and learned features, multilayer perceptron, back-propagation, activation functions, loss functions, computational analysis; Optimization techniques: Gradient descent methods, momentum, RMSProp, Adam; Convolutional Neural Networks (CNNs): Convolution, pooling, backpropagation in CNN, computational analysis; CNN architectures: LeNet, AlexNet, VGGNet, ResNet, GoogLeNet, DenseNet, MobileNet, EfficientNet; Training issues: Early stopping, dropout, batch normalization, instance normalization, group normalization; Sequential modelling: Recurrent neural networks (RNNs), LSTM, attention; Generative models: Autoencoders, generative adversarial networks (GANs);

• I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
• M. A. Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.

• A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola, Dive into Deep Learning, Cambridge University Press, 2021.
• Y. Bengio, Learning Deep Architectures for AI, Now Publishers Inc., 2009.

Wireless Communication Basics: Overview of physical, medium access control, and network layers and their functionalities; AI Basics: Supervised, unsupervised, and reinforcement learning, deep neural networks; AI-based techniques applied to wireless communication: Channel modelling, spectrum sensing, radio resource allocation, energy efficient communications, adaptive modulation, coding and caching; AI use cases: Multiple input multiple output (MIMO) communication and Internet of Things (IoT).

• To be updated as required.

• Y. Eldar, A. Goldsmith, D. Gündüz, and H. Vincent Poor, Machine Learning and Wireless Communications, 1st Edition, Cambridge University Press, 2022.
• F.-L. Luo, Machine Learning for Future Wireless Communications, 1st Edition, Wiley, 2020.
• Z. Qin et al., AI Empowered Wireless Communications: From Bits to Semantics, Proceedings of the IEEE, Vol. 112, No. 7, July 2024.

Time Value of Money: Discounting, present and future value, annuity; Valuation of Bonds: Features of bonds, cash flow, pricing, yield; term structure of interest rates; duration and convexity of bonds; Stock Valuation: Methods of valuation of equity, dividend discount models, relative valuation; Efficient Market Hypothesis (EMH); Risk and Returns: Expected return and risk, measures of risk; Portfolio Theory: Portfolio return and risk, multi-asset portfolio, Markowitz’s model, efficient frontier, capital market line; Performance Analysis; Asset Pricing: Capital Asset Pricing Model (CAPM), pricing using CAPM, implications of CAPM, beta, security market line, arbitrage pricing theory (APT).

• To be updated as required.

• R. Srivastava and A. Misra, Financial Management, 2nd Edition, Oxford University Press, 2011.
• M. Capinski and T. Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, 2nd Edition, Springer, 2010.
• J. C. Francis and D. Kim, Modern Portfolio Theory: Foundations, Analysis, and New Developments, 1st Edition, Wiley, 2013.
• J. C. Hull and S. Basu, Options, Futures and Other Derivatives, 11th Edition, Pearson, 2022.

This course aims to equip future managers and leaders with the knowledge, skills, and mindset required to lead individuals and teams effectively in dynamic organizational environments. The course integrates classical leadership theories with contemporary practices, focusing on self-leadership, interpersonal influence, ethical leadership, team leadership, change leadership, and inclusive leadership.

Introduction to Leadership: Meaning of leadership, leadership vs management, role of leaders; Leadership Theories: Trait, behavioral, and contingency theories; Emotional Intelligence and leadership; Leadership Styles and Approaches: Transformational, Transactional, Authentic, Servant leadership; Leader–Member Exchange (LMX); Leadership in change and crisis; Conflict management by leaders; Developing leadership capability: assessment tools such as 360-degree feedback and leadership inventories; Role of communication in persuasion; Motivation theories and their application by leaders.

• Northouse, P. G., Leadership: Theory and Practice, 9th Edition, Sage Publications, 2022.
• Yukl, G., Leadership in Organizations, 9th Edition, Pearson, 2020.

• Kouzes, J. M. & Posner, B. Z., The Leadership Challenge, Wiley, 2017.
• Mintzberg, H., Simply Managing, Berrett-Koehler, 2009.
• Bass, B. M. & Riggio, R., Transformational Leadership, Psychology Press, 2012.
• Goleman, D., Emotional Intelligence, Bantam Books, 2006.

This course introduces the fundamental principles, process classifications, materials, and applications of 3D printing technologies. Emphasis is placed on understanding machine architectures, process parameters, and design considerations for different materials and systems. Each module links core 3D printing concepts to their AI-enabled extensions, ranging from geometry generation and slicing to defect prediction and design automation.

Introduction to 3D printing and opportunities for data science and AI; Computer-aided process planning for 3D printing; Liquid-based 3D printing technologies; Sheet-based 3D printing technologies; Wire-based 3D printing technologies; Powder-based 3D printing technologies; Hybrid 3D printing technologies; Opportunities for digital twins; Predictive maintenance; Intelligent process planning through AI-assisted decision frameworks.

• L. Jyothish Kumar, Pulak M. Pandey, and David Ian Wimpenny (eds.), 3D Printing and Additive Manufacturing Technologies, Springer, Singapore, 2019.
• Ian Gibson, David Rosen, and Brent Stucker, Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing, Springer, New York, NY, 2015.

• Patri K. Venuvinod and Weiyin Ma, Rapid Prototyping: Laser-Based and Other Technologies, Springer Science & Business Media, 2013.
• Paul F. Jacobs, Fundamentals of Stereolithography, In Proceedings of the International Solid Freeform Fabrication Symposium, 1992.

A significant gap exists between deep learning models and their practical deployment. In real-world settings, beyond selecting appropriate architectures and datasets for training, models are modified in various ways to achieve efficiency goals such as speed, memory usage, and energy consumption. This course focuses on bridging the gap between deep learning theory and its efficient implementation on modern hardware platforms.

• Gain an understanding of hardware deployment of deep learning models.
• Gain an understanding of deep learning hardware architectures.
• Understand efficiency-oriented techniques and their trade-offs.

Deep learning refresher: non-convex optimization, backpropagation; Automatic differentiation; Neural network library abstractions; Introduction to parallel programming; Deep learning hardware: parallelism in data and hardware, CPUs vs GPUs vs microcontrollers, hardware selection, modern hardware, learning on multiple machines, asynchronous parallelism, decentralized and local stochastic gradient descent; Deep learning inference: sparsity and pruning, quantization, knowledge distillation, fine-tuning, neural architecture search; Training large models; Generative models implementation; Post-training optimizations: parameter-efficient fine-tuning.

• Andres Rodriguez, Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, 1st Edition, Morgan & Claypool Publishers, 2021.
• Simon J. D. Prince, Understanding Deep Learning, 1st Edition, The MIT Press, 2023.

• Geoff Hulten, Building Intelligent Systems: A Guide to Machine Learning Engineering, 1st Edition, Apress, 2018.

This course provides an in-depth understanding of advanced topics in deep learning for computer vision. Students revisit the fundamental building blocks of convolutional neural networks and encoder–decoder architectures, and explore attention mechanisms, transformer networks, and generative models. The course also introduces cutting-edge algorithms, enabling students to design, implement, and optimize state-of-the-art deep learning models for a wide range of computer vision applications.

Introduction: review of image processing basics; Revisiting convolutional neural networks (CNNs): building blocks of CNNs, backpropagation, encoder–decoder models; CNNs for vision tasks: detection, recognition, segmentation, inpainting, super-resolution, depth estimation, deepfakes; CNN and recurrent neural network (RNN) models for video understanding; Attention models: vision and language, image captioning, visual question answering (VQA); Transformer networks: multi-headed self-attention, cross-attention; Deep generative models: generative adversarial networks (GANs), variants of GANs, variational autoencoders (VAEs); Recent trends: zero-shot, one-shot, few-shot learning, self-supervised learning, meta-learning, transfer learning, and knowledge distillation in vision.

• R. Szeliski, Computer Vision: Algorithms and Applications, 2nd Edition, Springer, 2022.
• I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.

• Y. Bengio, Learning Deep Architectures for AI, Now Publishers Inc., 2009.
• A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola, Dive into Deep Learning, Cambridge University Press, 2021.