# Education

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## Three Examples of Ancient High Tech

(1) Antikythera mechanism, a 2000 year old computer.

The Antikythera mechanism is an ancient analog computer designed to calculate astronomical positions.

Fragment of the Antikythera mechanism

(2) Hero engine, a rocket style jet engine.

Hero’s engine

A Hero engine, is a rocket style jet engine which spins when heated. In the 1st century AD, Hero of Alexandria described the device, and many sources give him the credit for its invention.

The aeolipile Hero described is considered to be the first recorded steam engine or reaction steam turbine.

For an environment as prone to earthquakes as the Italian peninsula, interruptions and internal constructions within walls and domes created discontinuities in the concrete mass. Portions of the building could then shift slightly when there was movement of the earth to accommodate such stresses, enhancing the overall strength of the structure. It was in this sense that bricks and concrete were flexible. It may have been precisely for this reason that, although many buildings sustained serious cracking from a variety of causes, they continue to stand to this day.

The Pantheon in Rome, Italy, is an example of Roman concrete construction.

For more on Roman concrete, see here:

In a quest to make concrete more durable and sustainable, an international team of geologists and engineers has found inspiration in the ancient Romans, whose massive concrete structures have withstood the elements for more than 2,000 years.

The discovery could help improve the durability of modern concrete, which within 50 years often shows signs of degradation, particularly in ocean environments.

(4) Lycurgus Cup, a Roman glass cage cup made of a dichroic glass.

The particles are only about 70 nanometers across, and embedded in the glass, so they cannot be seen by optical microscopy, and a transmission electron microscope is needed instead.

The Lycurgus Cup

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## Personal curriculum

### Categories

1. Probability, Statistics, Discrete mathematics (Combinatorics) and R
2. Python
3. Calculus and Differential Equations
4. Linear Algebra
5. Logic
6. Miscellaneous Mathematics
7. Basic Mathematics
8. Lambda calculus, Haskell and Project Euler
9. Programming, Computer Science, Information Theory
10. C/C++
11. Miscellaneous 01
12. Miscellaneous 01

### Content Examples

1: Probability, Statistics, Discrete mathematics (Combinatorics) and R

2: Python

6: Miscellaneous Mathematics

• Geometry
• Foundations of mathematics
• Set theory
• Number theory
• Proof theory
• Hilbert space theory
• Maths 1001
• The Princeton Companion to Mathematics

7: Basic Mathematics

• Practice

9: Programming, Computer Science, Information Theory

11/12: Miscellaneous

### Rules

1. If you feel like doing something else, e.g. gaming, compare the fun you expect to have by doing so and the long-term benefits to instead spending the time on following one of the above educational activities.
2. Always only follow one activity per category and finish it before moving on within that category by replacing it with something else on the associated list.
3. You are allowed to follow an activity that does not fit into that category if and only if it is necessary to do so to be able to continue with the original activity. But such a detour should at most be as extensive as absolutely necessary to continue with the appropriate category. Everything else should be made up for later in its own relevant category.
4. Force yourself to pursue activity 1 and 2 for 40 minutes per activity each day.
5. If somehow possible pursue each activity 3-12 for 20 minutes per activity each day.
6. Strictly alternate between activities 3-12 to allocate the same amount of time to each activity.
7. The categories Miscellaneous 1,2 can also be used to assign more weight and thereby extend the studies of one of the other categories, if necessary.

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## Computer Science and Programming: Books and Resources

It is instrumentally useful to understand how computers work and how to program them.

Even if you are not interested to learn to program, you might want to be able to at least interpret the most basic pseudo code as you might come across various snippets of code.

Below you will find a list of links to books and other resources. Most categories are sorted by their difficulty in ascending order.

Resources that can be understood with little or no knowledge of computer science and programming are marked EASY.

## Novice: Getting Started

EASY

This section is meant for people with little to no background in mathematics, programming and computer science.

In this course you will learn key concepts in computer science and learn how to write your own computer programs in the context of building a web crawler. There is no prior programming knowledge needed for this course. Beginners welcome! At the end of this course you will have a rock solid foundation for programming in Python and built a working web crawler. This course will prepare you to take many of Udacity’s more advanced courses.

“Invent Your Own Computer Games with Python” is a free book (as in, open source) and a free eBook (as in, no cost to download) that teaches you how to program in the Python programming language. Each chapter gives you the complete source code for a new game, and then teaches the programming concepts from the example. “Invent with Python” was written to be understandable by kids as young as 10 to 12 years old, although it is great for anyone of any age who has never programmed before.

The book intends to show a layman the basic mechanical principles of how computers work, instead of merely summarizing how the different parts relate. He starts with basic principles of language and logic and then demonstrates how they can be embodied by electrical circuits, and these principles give him an opening to describe in principle how computers work mechanically without requiring very much technical knowledge. Although it is not possible in a medium sized book for layman to describe the entire technical summary of a computer, he describes how and why it is possible that elaborate electronics can act in the ways computers do. In the introduction, he contrasts his own work with those books which “include pictures of trains full of 1s and 0s.”

## How Computers Work

The book intends to show a layman the basic mechanical principles of how computers work, instead of merely summarizing how the different parts relate. He starts with basic principles of language and logic and then demonstrates how they can be embodied by electrical circuits, and these principles give him an opening to describe in principle how computers work mechanically without requiring very much technical knowledge. Although it is not possible in a medium sized book for layman to describe the entire technical summary of a computer, he describes how and why it is possible that elaborate electronics can act in the ways computers do. In the introduction, he contrasts his own work with those books which “include pictures of trains full of 1s and 0s.”

Indeed, the best way to understand how computers work is to build one from scratch, and this textbook leads students through twelve chapters and projects that gradually build a basic hardware platform and a modern software hierarchy from the ground up. In the process, the students gain hands-on knowledge of hardware architecture, operating systems, programming languages, compilers, data structures, algorithms, and software engineering. Using this constructive approach, the book exposes a significant body of computer science knowledge and demonstrates how theoretical and applied techniques taught in other courses fit into the overall picture.

The new standard for computer architects, designers, and industry management. This book offers a new approach to understanding computer architecture, emphasizing the quantitative aspects of design and practical trade-offs that must be made. Readers will learn the principles and engineering fundamentals that allow designers to make the right design choices.

• Write Great Code Volume I: Understanding the Machine

This, the first of four volumes, teaches important concepts of machine organization in a language-independent fashion, giving programmers what they need to know to write great code in any language, without the usual overhead of learning assembly language to master this topic. The Write Great Code series will help programmers make wiser choices with respect to programming statements and data types when writing software.

• Write Great Code Volume II: Thinking Low-Level, Writing High-Level

…a good question to ask might be “Is there some way to write high-level language code to help the compiler produce high-quality machine code?” The answer to this question is “yes” and Write Great Code, Volume II, will teach you how to write such high-level code. This volume in the Write Great Code series describes how compilers translate statements into machine code so that you can choose appropriate high-level programming language statements to produce executable code that is almost as good as hand-optimized assembly code.

Assembly is a low-level programming language that’s one step above a computer’s native machine language. Although assembly language is commonly used for writing device drivers, emulators, and video games, many programmers find its somewhat unfriendly syntax intimidating to learn and use. Since 1996, Randall Hyde’s The Art of Assembly Language has provided a comprehensive, plain-English, and patient introduction to assembly for non-assembly programmers. Hyde’s primary teaching tool, High Level Assembler (or HLA), incorporates many of the features found in high-level languages (like C, C++, and Java) to help you quickly grasp basic assembly concepts. HLA lets you write true low-level code while enjoying the benefits of high-level language programming.

## An Overview of Computer Programming

Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you’ll go beyond the syntax-and beyond the 20-minute tutorial you’ll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what’s essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you’ll solve a nontrivial problem, using techniques that show off the language’s most important features. As the book proceeds, you’ll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly–for example, finding the typing and programming models, decision structures, and how you interact with them.

The ubiquity of computers in everyday life in the 21st century justifies the centrality of programming languages to computer science education.  Programming languages is the area that connects the theoretical foundations of computer science, the source of problem-solving algorithms, to modern computer architectures on which the corresponding programs produce solutions.  Given the speed with which computing technology advances in this post-Internet era, a computing textbook must present a structure for organizing information about a subject, not just the facts of the subject itself.  In this book, Michael Scott broadly and comprehensively presents the key concepts of programming languages and their implementation, in a manner appropriate for computer science majors.

This well-respected text offers an accessible introduction to functional programming concepts and techniques for students of mathematics and computer science. The treatment is as nontechnical as possible, assuming no prior knowledge of mathematics or functional programming. Numerous exercises appear throughout the text, and all problems feature complete solutions.

This introduction to programming places computer science in the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process. This approach fosters a variety of skills–critical reading, analytical thinking, creative synthesis, and attention to detail–that are important for everyone, not just future computer programmers.The book exposes readers to two fundamentally new ideas. First, it presents program design guidelines that show the reader how to analyze a problem statement; how to formulate concise goals; how to make up examples; how to develop an outline of the solution, based on the analysis; how to finish the program; and how to test. Each step produces a well-defined intermediate product. Second, the book comes with a novel programming environment, the first one explicitly designed for beginners.

Using a dialect of the Lisp programming language known as Scheme, the book explains core computer science concepts, including abstraction, recursion, interpreters and metalinguistic abstraction, and teaches modular programming. The program also introduces a practical implementation of the register machine concept, defining and developing an assembler for such a construct, which is used as a virtual machine for the implementation of interpreters and compilers in the book, and as a testbed for illustrating the implementation and effect of modifications to the evaluation mechanism. Working Scheme systems based on the design described in this book are quite common student projects.

## Computer Science and Computation

There is a considerable, satisfying and underappreciated depth in the science of computing, it offers so much more than simply a driving licence for using a computer. The author of this book is a top researcher working on the fundamentals of informatics, and he has considerable experience teaching complex subjects in a simple manner. In this book he invites the reader to explore the science of computing, convinced that a good explanation of even the most basic techniques can demonstrate the power and magic of the underlying principles. The author starts with the development of computer science, algorithms and programming, and then explains and shows how to exploit the concepts of infinity, computability, computational complexity, nondeterminism and randomness. Finally, he offers some insight into emerging computing paradigms such as biological computing and quantum computing. This book is a fascinating read for students of all levels, and for those curious to learn about the science and magic of algorithmics and its role in other scientific disciplines.

Udacity is a private educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky, with the stated goal of democratizing education It is the outgrowth of free computer science classes offered in 2011 through Stanford University. As of May 2012 Udacity has six active courses. The first two courses ever launched on Udacity both started on 20th February, 2012, entitled “CS 101: Building a Search Engine”, taught by Dave Evans, from the University of Virginia, and “CS 373: Programming a Robotic Car” taught by Thrun. Both courses use Python.

Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming. The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.

This text provides a broad introduction to the realm of computers. Updated and expanded, “The New Turing Omnibus” offers 66 concise articles on the major points of interest in computer science theory, technology and applications. New for this edition are: updated information on algorithms, detecting primes, noncomputable functions, and self-replicating computers – plus completely new sections on the Mandelbrot set, genetic algorithms, the Newton-Raphson Method, neural networks that learn, DOS systems for personal computers, and computer viruses.

A bold experiment in distributed education, “Introduction to Artificial Intelligence” will be offered free and online to students worldwide from October 10th to December 18th 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford’s introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams. Artificial Intelligence is the science of making computer software that reasons about the world around it. Humanoid robots, Google Goggles, self-driving cars, even software that suggests music you might like to hear are all examples of AI. In this class, you will learn how to create this software from two of the leaders in the field. Class begins October 10.

Michael Sipser’s emphasis on unifying computer science theory – rather than offering a collection of low-level details – sets the book apart, as do his intuitive explanations. Throughout the book, Sipser builds students’ knowledge of conceptual tools used in computer science, the aesthetic sense they need to create elegant systems, and the ability to think through problems on their own.

Computability and Logic has become a classic because of its accessibility to students without a mathematical background and because it covers not simply the staple topics of an intermediate logic course, such as Godel’s incompleteness theorems, but also a large number of optional topics, from Turing’s theory of computability to Ramsey’s theorem. Including a selection of exercises, adjusted for this edition, at the end of each chapter, it offers a new and simpler treatment of the representability of recursive functions, a traditional stumbling block for students on the way to the Godel incompleteness theorems.

## Supplementary Resources: Mathematics and Algorithms

This book introduces the mathematics that supports advanced computer programming and the analysis of algorithms. The primary aim of its well-known authors is to provide a solid and relevant base of mathematical skills – the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle patterns in data. It is an indispensable text and reference not only for computer scientists – the authors themselves rely heavily on it! – but for serious users of mathematics in virtually every discipline.

The textbook Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne surveys the most important algorithms and data structures in use today.

Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

Software developers and computer scientists have eagerly awaited this comprehensive revision of Robert Sedgewick’s landmark texts on algorithms. Sedgewick has completely revamped all five sections, illuminating today’s best algorithms for an exceptionally wide range of tasks. This shrink-wrapped package brings together Algorithms in C, Third Edition, Parts 1-4 and his new Algorithms in C, Third Edition, Part 5. Together, these books are definitive: the most up-to-date and practical algorithms resource available. Sedgewick masterfully blends theory and practice, making Bundle of Algorithms in C, Third Edition an essential resource for computer science researchers, students, and working developers alike. The first book introduces fundamental concepts associated with algorithms; then covers data structures, sorting, and searching. The second book focuses entirely on graphing algorithms, which are critical for a wide range of applications, including network connectivity, circuit design, scheduling, transaction processing, and resource allocation. Together, these books present nearly 2,000 new exercises, hundreds of new figures, and dozens of new programs. In both books, Sedgewick focuses on practical applications, giving readers all the information, diagrams, and real (not pseudo-) code they need to confidently implement, debug, and use the algorithms he presents.

This work is not about computer programming in the narrow sense, but about the algorithms and methods which lie at the heart of most computer systems. At the end of 1999, these books were named among the best twelve physical-science monographs of the century by American Scientist, along with: Dirac on quantum mechanics, Einstein on relativity, Mandelbrot on fractals, Pauling on the chemical bond, Russell and Whitehead on foundations of mathematics, von Neumann and Morgenstern on game theory, Wiener on cybernetics, Woodward and Hoffmann on orbital symmetry, Feynman on quantum electrodynamics, Smith on the search for structure, and Einstein’s collected papers.

## Practice

Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems.

Python Challenge is a game in which each level can be solved by a bit of (Python) programming.

CodeChef is a global programming community. We host contests, trainings and events for programmers around the world. Our goal is to provide a platform for programmers everywhere to meet, compete, and have fun.

Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This is a deeply technical book and focuses on the software engineering skills to ace your interview. The book is over 500 pages and includes 150 programming interview questions and answers, as well as other advice.

## Programming Languages

Some important programming languages are the high-level scripting language Python that incorporates various programming paradigms such as iterative and functional programming. The purely functional programming language Haskell, based on lambda calculus. The highly efficient low-level programming language C. The very expressive LISP family of languages, including Scheme. And the specialized R language for statistical computing and graphics.

### Python

pyscripter is an open-source Python Integrated Development Environment (IDE)

• Khan Academy Introduction to programming and computer science (using Python) EASY

“Invent Your Own Computer Games with Python” is a free book (as in, open source) and a free eBook (as in, no cost to download) that teaches you how to program in the Python programming language. Each chapter gives you the complete source code for a new game, and then teaches the programming concepts from the example. “Invent with Python” was written to be understandable by kids as young as 10 to 12 years old, although it is great for anyone of any age who has never programmed before.

Have you always wanted to learn how to code but never thought you could? Are you looking to build a foundation for more complex coding? Do you want to challenge your brain in a new way? Then Learn Python the Hard Way is the book for you.

Think Python is an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters.

This book is suitable for use in a university-level first course in computing (CS1), as well as the increasingly popular course known as CS0. It is difficult for many students to master basic concepts in computer science and programming. A large portion of the confusion can be blamed on the complexity of the tools and materials that are traditionally used to teach CS1 and CS2. This textbook was written with a single overarching goal: to present the core concepts of computer science as simply as possible without being simplistic.

Computers are used in every part of science from ecology to particle physics. This introduction to computer science continually reinforces those ties by using real-world science problems as examples. Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer.

The Quick Python Book, Second Edition, is a clear, concise introduction to Python 3, aimed at programmers new to Python. This updated edition includes all the changes in Python 3, itself a significant shift from earlier versions of Python. The book begins with basic but useful programs that teach the core features of syntax, control flow, and data structures. It then moves to larger applications involving code management, object-oriented programming, web development, and converting code from earlier versions of Python.

The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example- and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology, and finance. The book teaches “Matlab-style” and procedural programming as well as object-oriented programming. High school mathematics is a required background, and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.

The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists.

The full details of an industrial-strength spell corrector are quite complex. What I wanted to do here is to develop, in less than a page of code, a toy spelling corrector that achieves 80 or 90% accuracy at a processing speed of at least 10 words per second.

The Haskell Platform is the easiest way to get started with programming Haskell. It comes with all you need to get up and running. Think of it as “Haskell: batteries included”.

Haskell is one of the leading languages for teaching functional programming, enabling students to write simpler and cleaner code, and to learn how to structure and reason about programs. This introduction is ideal for beginners: it requires no previous programming experience and all concepts are explained from first principles via carefully chosen examples. Each chapter includes exercises that range from the straightforward to extended projects, plus suggestions for further reading on more advanced topics. The author is a leading Haskell researcher and instructor, well-known for his teaching skills. The presentation is clear and simple, and benefits from having been refined and class-tested over several years. The result is a text that can be used with courses, or for self-learning. Features include freely accessible Powerpoint slides for each chapter, solutions to exercises and examination questions (with solutions) available to instructors, and a downloadable code that’s fully compliant with the latest Haskell release.

Learn You a Haskell, the funkiest way to learn Haskell, which is the best functional programming language around. You may have heard of it. This guide is meant for people who have programmed already, but have yet to try functional programming.

This easy-to-use, fast-moving tutorial introduces you to functional programming with Haskell. You’ll learn how to use Haskell in a variety of practical ways, from short scripts to large and demanding applications. Real World Haskell takes you through the basics of functional programming at a brisk pace, and then helps you increase your understanding of Haskell in real-world issues like I/O, performance, dealing with data, concurrency, and more as you move through each chapter.

The textbook by Doets and van Eijck puts the Haskell programming language systematically to work for presenting a major piece of logic and mathematics. The reader is taken through chapters on basic logic, proof recipes, sets and lists, relations and functions, recursion and co-recursion, the number systems, polynomials and power series, ending with Cantor’s infinities. The book uses Haskell for the executable and strongly typed manifestation of various mathematical notions at the level of declarative programming. The book adopts a systematic but relaxed mathematical style (definition, example, exercise, …); the text is very pleasant to read due to a small amount of anecdotal information, and due to the fact that definitions are fluently integrated in the running text. An important goal of the book is to get the reader acquainted with reasoning about programs.

### Common Lisp

GNU CLISP (compiler, runtime) is an implementation of the programming language Common Lisp. CLISP is extremely portable, running on almost all Unix-based operating systems as well as on Microsoft Windows.

Lisp has been hailed as the world’s most powerful programming language, but its cryptic syntax and academic reputation can be enough to scare off even experienced programmers. Those dark days are finally over—Land of Lisp brings the power of functional programming to the people! With his brilliantly quirky comics and out-of-this-world games, longtime Lisper Conrad Barski teaches you the mysteries of Common Lisp. You’ll start with the basics, like list manipulation, I/O, and recursion, then move on to more complex topics like macros, higher order programming, and domain-specific languages. Then, when your brain overheats, you can kick back with an action-packed comic book interlude!

Practical Common Lisp presents a thorough introduction to Common Lisp, providing you with an overall understanding of the language features and how they work. Over a third of the book is devoted to practical examples such as the core of a spam filter and a web application for browsing MP3s and streaming them via the Shoutcast protocol to any standard MP3 client software (e.g., iTunes, XMMS, or WinAmp). In other “practical” chapters, author Peter Seibel demonstrates how to build a simple but flexible in-memory database, how to parse binary files, and how to build a unit test framework in 26 lines of code.

Teaching users new and more powerful ways of thinking about programs, this two-in-one text contains a tutorial—full of examples—that explains all the essential concepts of Lisp programming, plus an up-to-date summary of ANSI Common Lisp, listing every operator in the language. Informative and fun, it gives users everything they need to start writing programs in Lisp both efficiently and effectively, and highlights such innovative Lisp features as automatic memory management, manifest typing, closures, and more. Dividing material into two parts, the tutorial half of the book covers subject-by-subject the essential core of Common Lisp, and sums up lessons of preceding chapters in two examples of real applications: a backward-chainer, and an embedded language for object-oriented programming. Consisting of three appendices, the summary half of the book gives source code for a selection of widely used Common Lisp operators, with definitions that offer a comprehensive explanation of the language and provide a rich source of real examples; summarizes some differences between ANSI Common Lisp and Common Lisp as it was originally defined in 1984; and contains a concise description of every function, macro, and special operator in ANSI Common Lisp. The book concludes with a section of notes containing clarifications, references, and additional code.

Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

Let Over Lambda is one of the most hardcore computer programming books out there. Starting with the fundamentals, it describes the most advanced features of the most advanced language: COMMON LISP. The point of this book is to expose you to ideas that you might otherwise never be exposed to.

These are Maxwell’s equations. Just four compact equations. With a little work it’s easy to understand the basic elements of the equations – what all the symbols mean, how we can compute all the relevant quantities, and so on. But while it’s easy to understand the elements of the equations, understanding all their consequences is another matter. Inside these equations is all of electromagnetism – everything from antennas to motors to circuits. If you think you understand the consequences of these four equations, then you may leave the room now, and you can come back and ace the exam at the end of semester […] I won’t stop with just showing you how to write some Lisp. Once we’ve done that we’re going to write an interpreter for Lisp code. In particular, we’ll create a interpreter based on a beautiful Lisp interpreter written by Peter Norvig, which contains just 90 lines of Python code. Our interpreter will be a little more complex, due mostly to the addition of a few conveniences absent from Norvig’s interpreter. The code is still simple and easy to understand, provided you’re comfortable reading Python code. As we’ll see, the benefit of writing the interpreter is not just that it gives us a running interpreter (although that’s no small thing). It’s that writing an interpreter also deepens our understanding of Lisp. It does that by taking what would otherwise be some rather abstract concepts in our description of Lisp, and giving them concrete, tangible representations in terms of Python code and data structures. By making concrete what was formerly abstract, the code for our Lisp interpreter gives us a new way of understanding how Lisp works.

### Scheme

To get started coding Scheme install Racket and select R5RS and R6RS (the standard dialects of Scheme).

The Little Schemer introduces computing as an extension of arithmetic and algebra ;things that everyone studies in grade school and high school. It introduces programs as recursive functions and briefly discusses the limits of what computers can do. The authors use the programming language Scheme, and interesting foods to illustrate these abstract ideas.

Scheme is a general-purpose programming language, descended from Algol and Lisp, widely used in computing education and research and a broad range of industrial applications. This thoroughly updated edition of The Scheme Programming Language provides an introduction to Scheme and a definitive reference for standard Scheme, presented in a clear and concise manner. Written for professionals and students with some prior programming experience, it begins by leading the programmer gently through the basics of Scheme and continues with an introduction to some of the more advanced features of the language.

### R

RStudio is a free and open source integrated development environment (IDE) for R. You can run it on your desktop (Windows, Mac, or Linux) or even over the web using RStudio Server.

R is a tool for statistics and data modeling. The R programming language is elegant, versatile, and has a highly expressive syntax designed around working with data. R is more than that, though — it also includes extremely powerful graphics capabilities. If you want to easily manipulate your data and present it in compelling ways, R is the tool for you.

Here is a list of FREE R tutorials hosted in official website of universities around the world.

Here you will find daily news and tutorials about R, contributed by over 300 bloggers.

R is the world’s most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you’ll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats.

The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks–t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Free software now includes programs in JAGS, which runs on Macintosh, Linux, and Windows.

### C/C++

Code::Blocks, a free C/C++ IDE (Integrated development environment) bundled with a MinGW compiler to produce Windows programs.

C Books

Ever wished there was an easier way to learn C from a book? Head First C is a complete learning experience that will show you how to create programs in the C language. This book helps you learn the C language with a unique method that goes beyond syntax and how-to manuals and helps you understand how to be a great programmer. You’ll learn key areas such as language basics, pointers and pointer arithmetic, and dynamic memory management, and with advanced topics such as multi-threading and network programming, Head First C can be used as an accessible text book for a college-level course.

Learn the C programming language from one of the best. Stephen Kochan’s Programming in C is thorough with easy-to-follow instructions that are sure to benefit beginning programmers. This book provides readers with practical examples of how the C programming language can be used with small, fast programs, similar to the programming used by large game developers such as Nintendo. If you want a one-stop-source for C programming, this book is it.The book is appropriate for all introductory-to-intermediate courses on programming in the C language, including courses covering C programming for games and small-device platforms.

Professor King’s spiral approach made it accessible to a broad range of readers, from beginners to more advanced students. With adoptions at over 225 colleges, the first edition was one of the leading C textbooks of the last ten years. The second edition maintains all the book’s popular features and brings it up to date with coverage of the C99 standard. The new edition also adds a significant number of exercises and longer programming projects, and includes extensive revisions and updates.

The new classic! C Primer Plus, now in its 5th edition, has been revised to include over 20 new programming exercises, newly improved examples and the new ANSI/ISO standard, C99. Task-oriented examples will teach you the fundamentals of C programming. From extended integer types and compound literals to Boolean support and variable-length arrays, you will learn to create practical and real-world applications with C programming. Review questions and programming exercises at the end of each chapter will reinforce what you have learned. This friendly and easy-to-use self-study guide will help you understand the fundamentals of this core programming language.

C++ Books

C++ Programming in Easy Steps makes no assumption you have previous knowledge of any programming language so it’s ideal for the newcomer to computer programming. It has an easy-to-follow style that will appeal to programmers moving from another programming language, and to the student who is studying C++ programming at school or college, and to those seeking a career in computing who need a fundamental understanding of object oriented programming.

Want to learn how to program in C++ immediately? Want to start writing better, more powerful C++ programs today? Accelerated C++’s uniquely modern approach will help you learn faster and more fluently than you ever believed possible. Based on the authors’ intensive summer C++ courses at Stanford University, Accelerated C++ covers virtually every concept that most professional C++ programmers will ever use — but it turns the “traditional” C++ curriculum upside down, starting with the high-level C++ data structures and algorithms that let you write robust programs immediately. Once you’re getting results, Accelerated C++ takes you “under the hood,” introducing complex language features such as memory management in context, and explaining exactly how and when to use them. From start to finish, the book concentrates on solving problems, rather than learning language and library features for their own sake. The result: You’ll be writing real-world programs in no time — and outstanding code faster than you ever imagined.

Written by Bjarne Stroustrup, the creator of C++, this is the world’s most trusted and widely read book on C++.

## A Guide to Bayes’ Theorem – A few links

### miscellaneous

A law of probability that describes the proper way to incorporate new evidence into prior probabilities to form an updated probability estimate. Bayesian rationality takes its name from this theorem, as it is regarded as the foundation of consistent rational reasoning under uncertainty. A.k.a. “Bayes’s Theorem” or “Bayes’s Rule”.

Eliezer Yudkowsky is on bloggingheads.tv with the statistician Andrew Gelman.

Several different points of fascination about Bayes…

When looking further, there is however a whole crowd on the blogs that seems to see more in Bayes’s theorem than a mere probability inversion…

Bayesian statistics is a system for describing epistemological uncertainty using the mathematical language of probability.
Bayesian probability is one of the most popular interpretations of the concept of probability.

Edwin T. Jaynes was one of the first people to realize that probability theory, as originated by Laplace, is a generalization of Aristotelian logic that reduces to deductive logic in the special case that our hypotheses are either true or false. This web site has been established to help promote this interpretation of probability theory by distributing articles, books and related material. As Ed Jaynes originated this interpretation of probability theory we have a large selection of his articles, as well as articles by a number of other people who use probability theory in this way…

Bayesian statistics is so closely linked with induction that one often hears it called “Bayesian induction.” What could be more inductive than taking a prior, gathering data, updating the prior with Bayes Law, and limiting to the true distribution of some parameter?

Gelman (of the popular statistics blog) and Shalizi point that, in practice, Bayesian statistics should actually be seen as Popper-style hypothesis-based deduction. The problem is intricately linked to the “taking a prior” above.

Or, how to recognize Bayes’ theorem when you meet one making small talk at a cocktail party.

Still, I’m sure Blogger won’t mind me using their resources instead. The basic idea is that there’s a distinction between true values x and measured values y. You start off with a prior probability distribution over the true values. You then have a likelihood function, which gives you the probability P(y|x) of measuring any value y given a hypothetical true value x.

In other words, What is so special about starting with a human-generated hypothesis? Bayesian methods suggest what I think is the right answer: To get from probabilistic evidence to the probability of something requires combining the evidence with a prior expectation, a “prior probability”, and human hypothesis generation enables this requirement to be ignored with considerable practical success.

Andrew Gelman recently responded to a commenter on the Yudkowsky/Gelman diavlog; the commenter complained that Bayesian statistics were too subjective and lacked rigor.  I shall explain why this is unbelievably ironic…

Maybe this kind of Bayesian method for “proving the null” could be used to achieve a better balance.

Bayesian brain is a term that is used to refer to the ability of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics.

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P.S.

Expect this link collection to be permanently updated.