In my experience, Ph.D. dissertations can be a wonderful resource for getting an overview of a particular academic topic. This is because the typical—and expected—pattern for a dissertation is to first survey the existing literature before diving into one’s own research. This both shows that the doctoral candidate has done his/her homework, and, just as importantly, brings his/her committee members up to speed on the necessary background. For example, a lot of my early education in Bayesian methods came from reading the doctoral dissertations of Wray Buntine, David J. C. MacKay, and Radford Neal on applications of Bayesian methods to machine learning. Michael Kearns’ dissertation helped me learn about computational learning theory. A philosophy dissertation helped me learn about temporal logic.
Of course, this requires that you already have some background in some related discipline. My background was in computer science when I read the above-mentioned dissertations, along with a pretty good foundation in mathematics.
Research moves fast though; a dissertation just 3 or 4 years old may already be hopelessly out of date. Also, they are written by PhD students who, while masters in their own field of expertise, are really only ‘apprentices’ in training and many not be very knowledgable about areas only slightly outside their domain.
Scientific journals often publish ‘review’ articles where people with decades of intimate knowledge about a field summarize recent developments. They are usually more concise than dissertations, and often written much better too. They are also peer-reviewed, just like dissertations and other papers.
In my experience, Ph.D. dissertations can be a wonderful resource for getting an overview of a particular academic topic. This is because the typical—and expected—pattern for a dissertation is to first survey the existing literature before diving into one’s own research. This both shows that the doctoral candidate has done his/her homework, and, just as importantly, brings his/her committee members up to speed on the necessary background. For example, a lot of my early education in Bayesian methods came from reading the doctoral dissertations of Wray Buntine, David J. C. MacKay, and Radford Neal on applications of Bayesian methods to machine learning. Michael Kearns’ dissertation helped me learn about computational learning theory. A philosophy dissertation helped me learn about temporal logic.
Of course, this requires that you already have some background in some related discipline. My background was in computer science when I read the above-mentioned dissertations, along with a pretty good foundation in mathematics.
Research moves fast though; a dissertation just 3 or 4 years old may already be hopelessly out of date. Also, they are written by PhD students who, while masters in their own field of expertise, are really only ‘apprentices’ in training and many not be very knowledgable about areas only slightly outside their domain.
Scientific journals often publish ‘review’ articles where people with decades of intimate knowledge about a field summarize recent developments. They are usually more concise than dissertations, and often written much better too. They are also peer-reviewed, just like dissertations and other papers.