I am wondering if anyone has any insight into this. I am thinking of going to grad school to get some computer science related degree. I have always been intrigued by people who are working on problems using statistical packages or simulation to solve problems. What would I study to get a good breadth of knowledge of these things? Do they fall into machine learning?
Thanks
My girlfriend is getting a degree in mathematics with an emphasis in Statistics and Operations Research.
She does a lot of work with SAS and other statistical software to maximize certain functions and predict the likelihood of future events. It may be more mathematics then you like, but you might try looking for masters of CS programs with an emphasis in Operations Research or Statistics.
There's a wide range of possible opportunities here. Let me add the following choices:
Physics with a focus on complex networks. This has applications in biology, epidemiology, sociology, finance, and computer science.
A good machine learning program, with statistics, data mining, text analysis, and computational learning theory.
Industrial engineering/operations research, with simulation, reliability, and process control.
I'd be happy to talk further about this, please put questions in comments.
I would assume that your school would offer some actual Statistics courses, probably in the Math department, which you could take to learn all about this.
Study a lot of mathematics, especially probability and statistics. I have a graduate simulation course right now, and I wish I knew more probs/stats stuff.
In Biostatics (at the U of Minnesota), we did a lot of simulation, in areas like Bayesian statistics, genetics, and others. Any strongly analytical program is a good candidate for teaching the skills you want, including: econ, econometrics, agronomics, statistical genetics... etc., etc., :)
While you're waiting, pick up R, Matlab (Octave is the free implementation), or your Turing-Complete language of choice, dig into Wikipedia, and get to work :)
I'd like to second Gregg Lind's recommendation of thinking about statistics in the biological sciences. It's well-funded, there's a lot of interesting work going on (both theoretical and applied!), and you can sound really cool at parties because somehow, someway you can always make some sort of connection from your work back to curing cancer. :)
Seriously though, a lot of great statistical work was done in the early 20th century by people like Haldane, Fiscer and Wright. More recent interesting work has been done on analysis or large data sets, multiple hypothesis testing, and applied machine learning. It's super exciting. Come join us!
Related
I've been trying to figure out what computer field I want to go into later on in life. College is just around the corner for me and I've considered looking into Computer Engineering, Software Engineering, etc.
Lately, I've been looking into computer security systems and exploitations of such (purely for educational purposes, on my own property). Unfortunately, it seems to me like 99% of the people out there have no idea what they're talking about. Oftentimes, it's just "run this" or "run that" or "you can find a program that will do all that for you" - no one knows how these programs work or what exactly they do.
I find no fun or interest in using something that someone else created simply to call myself a "hacker" as most people do. In fact, I'm not even interested in hacking systems as much as HOW they do it.
My question all comes down to this.
I want to learn the ins, outs, ups, and downs of computers - everything from abstract concepts such as the internet and data transferral, to hardware. I want to know how computers store data (how the bites are organized, etc.) and what processors, etc. actually do. What is WIFI, really? Do computers communicate with light (something I picked up from a magazine that I read on a plane).
I have multiple years of computer/programming experience, but so much of what I know about computers in general is very broad. Computers send packages of information back and forth between one another, each with a header and content. Computers are composed of multiple components, each with their own function (processor, video card, RAM, hard drive(s), etc.), which I have some basic understanding of already. etc. etc. etc.
There is just so much to a computer and I don't know where to start. I'm sure some of my college classes will clear things up for me, but I'm so curious that I want to start learning as much as I can now.
This question is probably all over the place, so please ask me to clarify when necessary. I'm a little jet lagged at the moment, but I tried to write my thoughts in the quickest, most coherent way possible (I could have completely failed in the process, though).
Thanks in advance for any advice!
Justian Meyer
Please, feel free to edit the tags for this question. The current ones are terrible.
EDIT:
All these comments are making me excited :). So much to learn, so much to explore :).
To help you choose which specialization to go into, I would very highly recommend computer engineering(Known as CMPE or CE in college course books). Your classes will take you to everything you just listed, and with electives you can delve deeper into whichever aspects you wish(such as security and networking).
In CMPE you will learn both software(C, C++, and some C#) and then hardware( maybe two electrical engineering classes). Once you get to assembly programming, you will start to learn how the two combine to make up everything else in any computer or embedded system. It will take you down to the bit level of memory, CPU, data buses, I/O, and so many other things. I am just starting to do Digital Design, and its ****ing glorious. From what you described, you will enjoy being a CMPE major greatly.
There's computer science majors and software engineers; there's electrical engineers; but there is no cell phone, GPS, or computer designed without computer engineers!
Structured Computer Organization, Tanenbaum
It is a great book and explains everything from a transistor to a Java virtual machine.
These two helped me understand how the OS and memory in general works.
I believe a lot of things are derived out of these 'simple mechanics.
1.Anatomy of a program in memory
2.Pushing the limits on Windows memory
Steve Gibson of security now has been doing a series of podcasts on computer basics.
http://www.grc.com/securitynow.htm Episode 233 "Let's Design a Computer (part 1)" up to the most recent one "What We'll Do for Speed".
Every other episode he does listener feedback and those are good to listen to too.
a few times (like right now) they interrupted the series if a important security news item comes up (like when that big SSL thing broke a few months ago)
Its a really good show and I recommend starting on 233 and working your way up, then starting over on episode 1. Has also done very good series on how a computer network works and how cryptography works. (Ep 203 will blow your mind when he talks about the Boyer & Moore
method of searching)
Since you are deciding where to go exactly, to be in software development or to become expert in hardware and networking, I would like to point out that in my opinion it is two different occupations and they require two different mindsets. Good hardware experts are usually not good programmers and good programmers almost always not experts in hardware and networking. So I would say don't try to embrace both, stick to one direction which is most suitable to your mindset. To pursue two rabbits would result in catching no one.
#Justian
I see, sorry I somewhat misunderstood you. Desire to understand intricacies of how code gets processed inside of hardware is a very natural one. When in college I was reading the book "How computer works" - it is fairly simple, even somewhat primitive book about general hardware functionality. But it can get you a broad look on the topic.
Another analogy came to mind. Say linguists research internal mechanics of language, but it is neuroscientists who research on how language signals get processed in brain. Two very different occupations. This is not to discourage you from learning hardware though, this is just to underline difference between two realms.
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Its always said that more you program, the better you become. Sounds good and true.
But I was wondering if there is a proven route to becoming a better programmer.
Something like:
Learn a
Learn b
Learn c > 'Now you are good to burn the engines'
Try stuff around based on your learning.
The answer might be similar to a CS course roadmap, but I want to hear from successful programmers who might want to pitch in with something notable.
Thanks
It's not true that practice makes perfect.
It's perfect practice that makes perfect.
If all you do is keep repeating the same bad practices again and again, you'll only make it possible to create bad code faster.
By all means keep coding. But at the same time be critical of everything you do. Always have a jaundiced eye that looks for ways to do things better. Read widely to get new ideas. Talk to others about how they do things. Look at other people's code, good and bad.
There's no "sure" way to learn anything that I know of. If there was, anyone could master this.
All questions are rhetorical and meant to stimulate thought.
Technical parts:
Design Patterns - There are probably some specific to a domain but generally these are useful ways of starting parts of an application. Do you know MVC or MVP?
Basic algorithm starting points - Divide and conquer, dynamic programming, recursion, creating special data types like a heap, being greedy, etc.
Problem solving skills - How easily can you jump in and find where a bug is? Can you think of multiple solutions to the problem?
Abstract modelling - How well can you picture things in your head in terms of code or classes when someone is describing a problem?
High level versus low level - How well do you understand when one wants something high or low? This is just something I'd toss out there as these terms get through around a lot, like a high level view of something or a low level language.
Process parts:
Agile - Do you know Scrum, XP, and other new approaches to managing software projects? How about principles like YAGNI, DRY and KISS? Or principles like SOLID? Ideas like Broken Windows?
Developer Environment - How well do you know the IDE you use? Source Control? Continuous Integration? Do you know the bottle necks on your machine in terms of being productive?
xDD - Do you know of TDD, BDD, and other developments driven from a paradigm?
Refactoring - Do you go back over your old code and make it better or do you tend to write once and then abandon your code?
Soft skills:
Emotional Intelligence - Can be useful for presentations and working with others mostly.
Passions/Motivation - Do you know what gets your juices flowing and just kick butt in terms of being productive? Do you know what you would like to do for many many years?
My main piece of advice would be: don't be afraid to rewrite your own code. Look at stuff you wrote even a month ago and you will see flaws and want to rewrite stuff.
Make sure that you understand some fundamentals: collections, equality, hashcodes etc. These are useful across pretty much all modern languages.
Depending on the language you use - use lint and metric tools and run them over your code. Not all their suggestions will be applicable but learning which are important and which are not is important. E.g FindBugs, PMD etc for Java.
Above all refine and keep refining your work. Don't treat your work as abandonware!
Learn your 1st programming language a new programming paradigm or a
find a mentor you can learn from
Apply what you've learnt in a real world project
Learn from your mistakes and successes and goto step one
The trick is knowing what to learn first:
Programming languages - this is the place to start bcause you cannot write software without knowing at least one of these. After you've mastered one language try learning another.
Programming paradigm - i.e. object oriented, dynamic/functional programming etc. Try to learn a new one with each new language.
Design concepts - S.O.L.I.D, design patterns as well as architectural concepts.
People skills - learn to communicate your ideas.
Team leadership - learn how to sweep others and how to become a team or technological lead.
After that the sky is the limit.
I would look at improving roughly in this order, in iterations with each building on the previous one:
Programming concepts. Understand things like memory management, pointers, stacks, variable scope, etc.
Languages. Work on mastering several modern languages.
Design concepts. Learn about design patterns. Practice using them.
Communication. Often-overlooked. You can only become a highly valued Software Engineer if you can communicate effectively with non-tech people. Learn to listen and understand the needs that people are expressing, translate that into a set of requirements and a technical design, but then explain what you understood (and designed) back to them, in terms they can understand, for validation before you code. This is not an easy one to master, but it is essential.
Architectural concepts. Learn to understand the big picture of large, complex systems.
Learning a programming language is in many ways similar to learning a spoken language. The only way to get good at it is to do it as often as possible. In other works
Practice, practice, read and then practice more
Take time to learn about all sorts of coding techniques, tools and programming wisdom. This I have found to be crucial to my development. It's to easy to just code away and feel productive. What about what could be if you just had some more knowledge / weaponry under your belt to bang out that next widget.
Knowledge/know how is our real currency. The more we know the more we can make a better decision about how something should be done and do it faster.
For example, learn about:
•Development Practices, Software Design, Estimation, Methodologies Business Analysis Database Design (there are a lot of great books out there and online resources)
•Read Code - Open Source Projects are a good place for this. Read
Programming blogs
•Try to participate on Open Source
Projects.
•Look for programming user groups in
your town and/or someone who can mentor you.
And yes, as mentioned practice. Don't just read, do and watch how you will improve. :)
Practice, practice, practice.
Once you're over the basic hump of being able to program, you can also read useful books (i.e. Code Complete, Effective Java or equivalents, etc.) for ideas on how to improve your code.
First and foremost write code. Write as much as you can. Tackle hard problems. If you want to be a really good programmer you need to get into the guts of what you are doing. Spend a lot of time in debuggers looking at how things work. If you want to be a good programmer who really understands what is going on you need to get down to the metal and write highly async code, learn about how processors work and why SSE is so awesome. Understand threading primitives and be able to write them as well as describe what is actually happening in the processor. I could keep going here but you get the idea.
Second find someone who knows a lot more than you and learn. This relationship will work better if you are already deeply immersed in writing lots of code.
Third, spend some time in a large high quality open source code base. I learned a ton from the Quake I and Quake II code. Helped me be a better programmer.
Fourth take on hard problems. Push your limits. Build things that you thought were impossible. Right now I am writing a specialized compiler. I have learned so much just working on this for the last couple of months.
Sure, strictly speaking, the more you practice programming, the better you become at solving those sorts of problems. But is that what you really want?
Programming is a human activity more than a technological one, at its heart. It's easy to improve your computer skills, not so hard to improve your interpersonal skills.
Read "Journey of the Software Professional" by Hohmann. One of the concepts the concepts Hohmann describes is the "cognitive library," which includes both programming skills and non-programming skills. Expand your cognitive library, and your programming skill will improve too.
Read a lot of non-programming books too, and observe the world around you. Creating useful metaphors is an essential skill for the successful programmer. Why do restaurants do things how they do? What trade-offs is the garbage department making when they pick up the garbage every few days instead of every day? How does scaling affect how a grocery store does business? Be an inquisitive human to be a better programmer.
For me, there has to be a reason to learn something new... that is, unless I have a project in mind or some problem I need to solve, there's no hope. If that prerequisite is met, then I usually try to get "Hello, world" working, and after that the sky's the limit. So much of development these days is just learning new APIs. Occasionally there's some kind of paradigm shift that blows your mind, but that's not as common as people like to think, IMHO.
Find a program that intrigues you, one that solves a problem, or one that would simplify many of your tasks. Try to write something similar. You'll get up to speed very quickly and have fun doing it at the same time.
You can try learning one thing really well and then expanding out to programming areas that are associated with the things that you have learnt, so that you can offer complete solutions to customers.
At the same time, devote part of your time to explore things outside your comfort zone.
One you have learned something, try to learn something a little harder. Read and practice a lot about things that seem confusing at first time (lambda functins, threading, array manipulation, etc). It will take its time, but once you have practiced enough, what seemed confusing at first, will be familiar and easy.
In addition to the rest of the great advice already given here, don't be afraid to read about coding and good practice, but also take everything with a grain of salt and see what works best for you. A lot of advice is opinion.
Good sites to read:
-thedailywtf.com
-joelonsoftware.com
-codinghorror.com
-blogs.msdn.com/oldnewthing
A great place to get practice is programming competition websites. Those will help you learn how to write good algorithms, not necessarily maintainable code, but they're still a good place to start for learning.
The one I used to use (back when I had time) was:
http://uva.onlinejudge.org/
Learn more than one language. One at a time, definitely, but ultimately you should be fluent in a couple. This will give you a better perspective I think, and help you to become an expert at programming, rather than being an expert at a certain language.
Learn the ins and outs of computers at all levels, hardware, os, etc. Ideally you should be able to build your own system, install multiple operating systems on it, and diagnose just about every problem that can arise. I know many programmers who are not "computer tech people" and their failure to understand what is happening at every level becomes a major hindrance in diagnosing and fixing unusual bugs or performance issues.
As well as looking at 'last weeks code', talk to users of your work after delivery - be one yourself if possible.
Its not my bag, but some of the best coders I know have spent time supporting applications. The experience improved their product I'm sure.
eat breath dream the programming language your using (no seriously, it helps)
There are two kinds of learning -
1. Informal (like how you learned how to function in society- through interaction with peers and family)
2. Formal (like your high school training- through planned instruction)
If you want an entry-level programming job, formal training via an undergrad Computer Science/Engineering degree is the way to go. However, if you want to become a rock-star developer, it is best done by informal training- make unintentional mistakes and have senior developers curse at you, learn a design pattern because an app you are updating uses it, almost cry because a bad developer wrote a huge messy program lacking documentation and best practices and now you have to do several updates to it ASAP; thing of these nature.
It is hard for anyone to give you a list of all you need to know. It varies per area (e.g. a web developer vs. to a desktop developer) and it varies per company (e.g. Microsoft that sells software vs. General Motors that mainly just use it in their cars.) Informal traiing and being engaged in trying to learn to do your job better and get promoted is your best bet in my opinion.
To prove my point, everyone here has great answers but they all differ. Ask a rock-star developer how he learned something or when, why; they may not know- things just happen.
Practice, individually and collectively
Keep an open mind, always learn new things, don't limit yourself to what's familiar. Not solely from a tech perspective, ui design, people skills, ... Don't be afraid of what's new
Peer review, talk to people about your code, let people talk to you about their code, everyone has a unique way of looking at a problem and you will learn a great deal from peers
Love coding. If you love what you're doing, putting in alot of time seems effortless. Every coder needs the drive!
One small addition to these good answers. When I work on someone else's code, usually I pick up something new. If you have the opportunity to work with someone else that is of equal or greater skill, noticing their programming style can teach you tons.
For example, in C++ & Javascript I no longer use if() statements without braces. The reason is that it's just too easy to mistakenly put:
while (true) {
if (a > b)
print a
print b
}
This is an obvious typo, but very easy to introduce, especially if you're editing existing code. I just call it defensive programming in my mind, but little tricks like this are valuable at making you better.
So, find a peer or mentor, and work on their code.
I am not sure if the OP was looking for general advice on how to be a good programmer, but rather something more specific.
I know I am reviving this thread, but I found it because I was trying to see if anyone asked this question already.
What I had in mind was, can we come up with a "knowledge-map" of programming concepts similar to the map that Khan Academy uses.
As a programmer, I want to be able to visualize the dependencies and relationships between different ideas, so that I can understand what skill level I am currently at; what I need to know before tackling a challenging subject; and be able to visualize my progress.
The very belief in the roadmap's existence blocks the road to perfection.
Is there a research paper/book that I can read which can tell me for the problem at hand what sort of feature selection algorithm would work best.
I am trying to simply identify twitter messages as pos/neg (to begin with). I started out with Frequency based feature selection (having started with NLTK book) but soon realised that for a similar problem various individuals have choosen different algorithms
Although I can try Frequency based, mutual information, information gain and various other algorithms the list seems endless.. and was wondering if there an efficient way then trial and error.
any advice
Have you tried the book I recommended upon your last question? It's freely available online and entirely about the task you are dealing with: Sentiment Analysis and Opinion Mining by Pang and Lee. Chapter 4 ("Extraction and Classification") is just what you need!
I did an NLP course last term, and it came pretty clear that sentiment analysis is something that nobody really knows how to do well (yet). Doing this with unsupervised learning is of course even harder.
There's quite a lot of research going on regarding this, some of it commercial and thus not open to the public. I can't point you to any research papers but the book we used for the course was this (google books preview). That said, the book covers a lot of material and might not be the quickest way to find a solution to this particular problem.
The only other thing I can point you towards is to try googling around, maybe in scholar.google.com for "sentiment analysis" or "opinion mining".
Have a look at the NLTK movie_reviews corpus. The reviews are already pos/neg categorized and might help you with training your classifier. Although the language you find in Twitter is probably very different from those.
As a last note, please post any successes (or failures for that matter) here. This issue will come up later for sure at some point.
Unfortunately, there is no silver bullet for anything when dealing with machine learning. It's usually referred to as the "No Free Lunch" theorem. Basically a number of algorithms work for a problem, and some do better on some problems and worse on others. Over all, they all perform about the same. The same feature set may cause one algorithm to perform better and another to perform worse for a given data set. For a different data set, the situation could be completely reversed.
Usually what I do is pick a few feature selection algorithms that have worked for others on similar tasks and then start with those. If the performance I get using my favorite classifiers is acceptable, scrounging for another half percentage point probably isn't worth my time. But if it's not acceptable, then it's time to re-evaluate my approach, or to look for more feature selection methods.
I've noticed that a number of top universities are offering courses where students are taught subjects relating to Computer Graphics for their CS majors. Sadly this is something not offered by my university and something I would really like to get into sometime in the next couple of years.
A couple of the projects I've found from some universities are great, although I'm mostly interested in two things:
Raytracing:
I want to write a Raytracer within the next two years. What do I need to know? I'm not a fantastic programmer yet (Java, C and Prolog are my main languages as of today) but I'm slowly learning every day. Also, my Math background isn't all that great, so any pointers on books to read or advice on writing such a program would be fantastic. I tend to pick these things up pretty quickly so feel free to chuck references at me.
Programming 3D Rendered Models
I've looked at a couple of projects where students have developed models and used them in games. I've made a couple of 2D games with raster images but have never worked with 3D models. What would I need to learn in regards to programming these models? If it helps I used to be okay with 3D Studio Max and Cinema4D (although every single course seems to use Maya), but haven't touched it in about four years.
Sorry for posting such vague and, let's be honest, stupid questions. It's just something I've wanted to do for a while and something that'd be good as a large project for me to develop in my own time.
Related Questions
Literature and Tutorials for Writing a Ray Tracer
I can recommend pbrt, it's a book and a physically-based renderer used to teach computer science graduates. The description of the maths used is nice and clear, and since it is written in the 'literate programming' you can see the appropriate code (in C++) too.
The book "Computer Graphics: Principles and Practice" (known in the Computer Graphics circles as the "Foley-VanDam") is the basic for most computer graphics courses, and it covers the topic of implementing a ray-tracer in much detail. It is quite dated, but it's still the best, afaik, and the basic principles remain the same.
I also second the recommendation for Eric Lengyel's Mathematics for 3D Game Programming and Computer Graphics. It's not as thorough, but it's a wonderful review of the math basics you need for 3D programming, it has very useful summaries at the end of each chapter, and it's written in an approachable, not too scary way.
In addition, you'll probably want some OpenGL or DirectX basics. It's easier to start working with a 3D API, then learn the underlying maths than the opposite (in my opinion), but both options are possible. Just look for OpenGL on SO and you should find a couple of good references as well.
The 2000 ICFP Programming Contest asked participants to build a ray tracer in three days. They have a good specification for a simple ray tracer, and you can get code for the winning entries and some other entries as well. There were entries in a large number of different programming languages. This might be a nice way for you to get started.
The briefest useful answer I can give is that most of the important algorithms can be found in Real-Time Rendering by Tomas Akenine-Möller, Eric Haines, and Naty Hoffman, and the bibliography at the end has references to the necessary maths. Their website has a recommended reading list as well.
The most useful math book I've read on the subject is Eric Lengyel's Mathematics for 3D Game Programming and Computer Graphics. The maths you need most are geometry (obviously) and linear algebra (for dealing with all the matrices).
I took such a class last year, and I believe that the class was wonderful for forcing students to learn the math behind the computer graphics - not just the commands for making a computer do what you want.
My professor has a site located here and it has his lecture notes and problem sets that you can take a look through.
Our final project was indeed a raytracer, but once you know the mathematics behind it, coding (an inefficient one) is trivial.
For a mathematical introduction into these topics, see
http://graphics.idav.ucdavis.edu/education/GraphicsNotes/homepage.html
Check http://www.scratchapixel.com/lessons/3d-basic-lessons/lesson-1-writing-a-simple-raytracer/
This is a very good place to learn about ray tracing and rendering in general.
Does anyone known of a a good reference for canonical CS problems?
I'm thinking of things like "the sorting problem", "the bin packing problem", "the travailing salesman problem" and what not.
edit: websites preferred
You can probably find the best in an algorithms textbook like Introduction to Algorithms. Though I've never read that particular book, it's quite renowned for being thorough and would probably contain most of the problems you're likely to encounter.
"Computers and Intractability: A guide to the theory of NP-Completeness" by Garey and Johnson is a great reference for this sort of thing, although the "solved" problems (in P) are obviously not given much attention in the book.
I'm not aware of any good on-line resources, but Karp's seminal paper Reducibility among Combinatorial Problems (1972) on reductions and complexity is probably the "canonical" reference for Hard Problems.
Have you looked at Wikipedia's Category:Computational problems and Category:NP Complete Problems pages? It's probably not complete, but they look like good starting points. Wikipedia seems to do pretty well in CS topics.
I don't think you'll find the answers to all those problems in only one book. I've never seen any decent, comprehensive website on algorithms, so I'd recommend you to stick to the books. That said, you can always get some introductory material on canonical algorithm texts (there are always three I usually recommend: CLRS, Manber, Aho, Hopcroft and Ullman (this one is a bit out of date in some key topics, but it's so formal and well-written that it's a must-read). All of them contain important combinatorial problems that are, in some sense, canonical problems in computer science. After learning some fundamentals in graph theory you'll be able to move to Network Flows and Linear Programming. These comprise a set of techniques that will ultimately solve most problems you'll encounter (linear programming with the variables restricted to integer values is NP-hard). Network flows deals with problems defined on graphs (with weighted/capacitated edges) with very interesting applications in fields that seemingly have no relationship to graph theory whatsoever. THE textbook on this is Ahuja, Magnanti and Orlin's. Linear programming is some kind of superset of network flows, and deals with optimizing a linear function on variables subject to restrictions in the form of a linear system of equations. A book that emphasizes the relationship to network flows is Bazaraa's. Then you can move on to integer programming, a very valuable tool that presents many natural techniques for modelling problems like bin packing, task scheduling, the knapsack problem, and so on. A good reference would be L. Wolsey's book.
You definitely want to look at NIST's Dictionary of Algorithms and Data Structures. It's got the traveling salesman problem, the Byzantine generals problem, the dining philosophers' problem, the knapsack problem (= your "bin packing problem", I think), the cutting stock problem, the eight queens problem, the knight's tour problem, the busy beaver problem, the halting problem, etc. etc.
It doesn't have the firing squad synchronization problem (I'm surprised about that omission) or the Jeep problem (more logistics than computer science).
Interestingly enough there's a blog on codinghorror.com which talks about some of these in puzzle form. (I can't remember whether I've read Smullyan's book cited in the blog, but he is a good compiler of puzzles & philosophical musings. Martin Gardner and Douglas Hofstadter and H.E. Dudeney are others.)
Also maybe check out the Stony Brook Algorithm Repository.
(Or look up "combinatorial problems" on google, or search for "problem" in Wolfram Mathworld or look at Hilbert's problems, but in all these links many of them are more pure-mathematics than computer science.)
#rcreswick those sound like good references but fall a bit shy of what I'm thinking of. (However, for all I know, it's the best there is)
I'm going to not mark anything as accepted in hopes people might find a better reference.
Meanwhile, I'm going to list a few problems here, fell free to add more
The sorting problem Find an order for a set that is monotonic in a given way
The bin packing problem partition a set into a minimum number of sets where each subset is "smaller" than some limit
The travailing salesman problem Find a Hamiltonian cycle in a weighted graph with the minimum total weight