# CS2013: Background Information (optional reading)

The Lectures and Practicals of this course are meant to be self-explanatory. Having said that, you might benefit from reading (or just browsing) other things, so here are some optional suggestions:

- Rod Haggarty,
*Discrete Mathematics for Computing*This is a comparatively simple book on Discrete Mathematics, suitable if you want to brush up on stuff that was covered during your first year. See amazon . - Ken Rosen,
*Discrete Mathematics*. A more serious and extensive text on the same (wide!) topic area. See amazon . - Hopcroft, Motwani, and Ullman,
*Introduction to Automata Theory, Languages, and Computation*. A deep and extensive book, easily the most thorough of the ones we're listing here. Still, if you're looking to dot the i's in formal languages (including regular expressions etc.) then this book can be useful. Also, have a browse to get an impression of how this relates to models of computing. See amazon . - D.G.Rees,
*Essential Statistics*. A short and easy-going book. Suitable for plugging gaps in your background, while also showing a glimpse of how statistics is applied in hypothesis testing (so going beyond the course in this respect). See amazon . - Stephen Gorard, ``Revisiting a 90-year-old debate: the advantages of the mean deviation". A useful article on different ways in which the amount of variation in a sample can be measured. This would be useful to read if you're curious why the definition of Standard Deviation isn't a bit more straightforward than it is. (Understanding the argument in the article is not required for the CS2013 exam.)
- K.B.Korb and A.E.Nicholson,
*Bayesian Artificial Intelligence*Second Edition. A useful book on Bayesian Networks and their background. See amazon .

Concerning the logic lectures, you might benefit from these summary sheets:

Truth Conditions in docx and in pdf

Natural Deduction rules in docx and in pdf

Equivalence Rules in docx and in pdf