e advanced reader might find useful teaching ideas for undergraduate courses. Chapter 3 addresses the tricky concept of information measure. We learn something that everyone intuitively knows, namely, that there is no or little information in events that are certain or likely to happen. Uncertainty, on the other hand, is associated with high information contents. When several possible events are being considered, the correct information measure becomes entropy (Chapters 4-6). As shown, Shannon's entropy concept in IT is not with- out strong but subtle connections with the world of Boltzmann's thermodynamics. But Introduction IT goes a step further with the key notion of mutual information, and other useful entropy definitions (joint, conditional, relative), including those related to continuous random variables (differential). Chapter 7, on algorithmic entropy (or equivalently, Kolmogorov complexity), is meant to be a real treat. This subject, which comes with its strange Turing machines, is, however, reputedly difficult. Yet the reader should not find the presenta- tion level different from preceding material, thanks to many supporting examples. The conceptual beauty and reward of the chapter is the asymptotic convergence between Shannon's entropy and Kolmogorov's complexity, which were derived on completely independent assumptions! Chapters 8-10 take on a tour of information coding, which is primarily the art of compressing bits into shorter sequences. This is where IT finds its first and everlasting success, namely, Shannon's source coding theorem, leading to the notion of coding opti- mality. Several coding algorithms (Huffmann, integer, arithmetic, adaptive) are reviewed, along with a daring appendix (Appendix attempting to convey a comprehensive flavor in both audio and video standards. With Chapter 11, we enter the magical world of error correction. For the scientist, unlike the telecom engineer, it is phenomenal that bit errors coming from random physical events can be corrected with 100% accuracy. Here, 