To do or not to do a PhD? : insight and guidance from a public health phd graduate / / Sarah Cuschieri

• Record Nr.: UNINA9910483995903321
• Autore: Cuschieri Sarah
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-64671-8
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (XI, 66 p. 7 illus., 6 illus. in color.)
• Collana: SpringerBriefs in Public Health, , 2192-3698
• Disciplina: 362.1
• Soggetti: Public health; Professional education; Vocational education
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1. What Is a PhD? Am I Ready for this Commitment? -- Chapter 2. The Initial Steps Towards a PhD -- Chapter 3. Proposal, Permissions and Funding -- Chapter 4. The Fieldwork -- Chapter 5. The Art of Data Analysis -- Chapter 6. Putting Pen to Paper to Publication -- Chapter 7. Writing the Thesis -- Chapter 8. The Hurdles Along the Way – A Personal Experience -- Chapter 9. Getting Ready for the Oral Defence -- Chapter 10. What Comes After the Completion of a PhD?
• Sommario/riassunto: This book prepares and guides individuals who are about to embark (or already have embarked) on a health/medical PhD journey, with a specific focus on Public Health. Based on the author's experience as a recently graduated Doctor of Philosophy (PhD) student, readers benefit from the knowledge imparted and lessons learned, including an analysis of the different aspects of a Public Health doctoral degree, and practical tips and guidance on how to go about this journey from the initial phase of choosing a research niche up until the oral examination (also called defence). All throughout the book, the author shares examples from her own journey to show that in spite of sacrifices and hurdles along the way, hard work, perseverance, and supportive resources can help see you through, eventually, to a hopefully positive outcome at the end. Using an informal style, the author provides a ; step-wise guide, from chapter to chapter, on the various essential aspects that need to be considered, including: The initial steps towards a PhD Proposal, permissions and funding The fieldwork The art of data analysis The hurdles along the way – a personal experience What comes after the completion of a PhD? Intended to be a compact go-to guide for students throughout their PhD journey, both from an academic and personal perspective, To Do or Not to Do a PhD? engages readers who are about to enroll in or who already have started a PhD, especially in public health, epidemiology, and health/medical fields of study. The brief also would appeal to postgraduate and undergraduate students who are interested in learning about how to write a research proposal, draft a scientific paper for publication in a journal, or prepare a thesis.

Algorithmic Learning Theory : 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings / / edited by Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann

• Record Nr.: UNINA9910484805303321
• Pubbl/distr/stampa: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010
• ISBN: 1-280-38945-1; 9786613567376; 3-642-16108-1
• Edizione: [1st ed. 2010.]
• Descrizione fisica: 1 online resource (XIII, 421 p. 45 illus.)
• Collana: Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 6331
• Altri autori (Persone): HutterMarcus
• Disciplina: 006.3/1
• Soggetti: Artificial intelligence; Computer programming; Machine theory; Algorithms; Computer science; Artificial Intelligence; Programming Techniques; Formal Languages and Automata Theory; Theory of Computation; Computer Science Logic and Foundations of Programming
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Editors’ Introduction -- Editors’ Introduction -- Invited Papers -- Towards General Algorithms for Grammatical Inference -- The Blessing and the Curse of the Multiplicative Updates -- Discovery of Abstract Concepts by a Robot -- Contrast Pattern Mining and Its Application for Building Robust Classifiers -- Optimal Online Prediction in Adversarial Environments -- Regular Contributions -- An Algorithm for Iterative Selection of Blocks of Features -- Bayesian Active Learning Using Arbitrary Binary Valued Queries -- Approximation Stability and Boosting -- A Spectral Approach for Probabilistic Grammatical Inference on Trees -- PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation -- Inferring Social Networks from Outbreaks -- Distribution-Dependent PAC-Bayes Priors -- PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar -- A PAC-Bayes Bound for Tailored Density Estimation -- Compressed Learning with Regular Concept -- A Lower Bound for Learning Distributions Generated by Probabilistic Automata -- Lower Bounds on Learning Random Structures with Statistical Queries -- Recursive Teaching Dimension, Learning Complexity, and Maximum Classes -- Toward a Classification of Finite Partial-Monitoring Games -- Switching Investments -- Prediction with Expert Advice under Discounted Loss -- A Regularization Approach to Metrical Task Systems -- Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations -- Learning without Coding -- Learning Figures with the Hausdorff Metric by Fractals -- Inductive Inference of Languages from Samplings -- Optimality Issues of Universal Greedy Agents with Static Priors -- Consistency of Feature Markov Processes -- Algorithms for Adversarial Bandit Problems with Multiple Plays -- Online Multiple KernelLearning: Algorithms and Mistake Bounds -- An Identity for Kernel Ridge Regression.
• Sommario/riassunto: This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent ; data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it wasco-located and held in parallel with Algorithmic Learning Theory.