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Nomination of Jeffrey M. Lang
Distributed to some depository libraries in microfiche.
Unlocking Biomarker Identification - Harnessing AI and ML for Precision Medicine
Computational techniques to analyze genetic data for identifying biomarkers. These biomarkers are crucial for diagnosing diseases, predicting outcomes, and personalizing treatments. The book covers various machine learning algorithms, such as deep learning, support vector machines, and random forests, explaining how they can be applied to genomic datasets. It discusses feature selection methods, data pre-processing, and the challenges of dealing with high-dimensional data. Case studies and real-world applications illustrate the practical aspects. Additionally, the book addresses ethical considerations and data privacy issues. It is an invaluable resource for bioinformaticians, computational biologists, and healthcare professionals seeking to harness machine learning for genomic
Modern Compiler Implementation in ML
This new, expanded textbook describes all phases of a modern compiler: lexical analysis, parsing, abstract syntax, semantic actions, intermediate representations, instruction selection via tree matching, dataflow analysis, graph-coloring register allocation, and runtime systems. It includes good coverage of current techniques in code generation and register allocation, as well as functional and object-oriented languages, that are missing from most books. In addition, more advanced chapters are now included so that it can be used as the basis for two-semester or graduate course. The most accepted and successful techniques are described in a concise way, rather than as an exhaustive catalog of...
Software Verification and Formal Methods for ML-Enabled Autonomous Systems
This book constitutes the refereed proceedings of the 5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022, and the 15th International Workshop on Numerical Software Verification, NSV 2022, which took place in Haifa, Israel, in July/August 2022. The volume contains 8 full papers from the FoMLAS 2022 workshop and 3 full papers from the NSV 2022 workshop. The FoMLAS workshop is dedicated to the development of novel formal methods techniques to discussing on how formal methods can be used to increase predictability, explainability, and accountability of ML-enabled autonomous systems. NSV 2022 is focusing on the challenges of the verification of cyber-physical systems with machine learning components.
AI-ML for Decision and Risk Analysis
This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic complexities. These include skill acquisition, uncertain and time-consuming implementation of intended actions, open-world uncertainties about what might happen next and what consequences actions can have, and learning to cope effectively with uncertain and changing environments. The result is a more robust and implementable technology for AI/ML-assisted decision-making. The book is intended to inform a wide audience in related applied areas and to provide a fun and stimulating resource for students, researchers, and academics in data science and AI-ML, decision analysis, and other closely linked academic fields. It will also appeal to managers, analysts, decision-makers, and policymakers in financial, health and safety, environmental, business, engineering, and security risk management.
Small Teaching
Employ cognitive theory in the classroom every day Research into how we learn has opened the door for utilizing cognitive theory to facilitate better student learning. But that's easier said than done. Many books about cognitive theory introduce radical but impractical theories, failing to make the connection to the classroom. In Small Teaching, James Lang presents a strategy for improving student learning with a series of modest but powerful changes that make a big difference—many of which can be put into practice in a single class period. These strategies are designed to bridge the chasm between primary research and the classroom environment in a way that can be implemented by any facult...
Cheating Lessons
Cheating Lessons is a guide to tackling academic dishonesty at its roots. James Lang analyzes the features of course design and classroom practice that create cheating opportunities, and empowers teachers to build more effective learning environments. Instructors who curb academic dishonesty become better educators in other ways as well.
Multilevel Modeling of Secure Systems in QoP-ML
Introducing the Quality of Protection Modeling Language (QoP-ML), this book provides for the abstraction of security systems while maintaining emphasis on the details of quality protection . It delineates the steps used in cryptographic protocol and introduces a multilevel protocol analysis that expands current understanding. Every operation defined by QoP-ML is described within parameters of security metrics, therefore evaluating the impact of the operation on the entire system's security.
