learned in the class.
Information measures: entropy, mutual information, Kullback-Libler divergence, data processing inequality.
Typical sequences: method of typical sequences as a combinatorical approach for bounding error probabilities.
Lossless source
coding (data compression) : block coding, data compression
using typical sets.
Information
transmission via noisy medium: channel capacity,
capacity computation, channel coding theorem,
converse
to the channel coding theorem, channels with feedback.
Lossy source
coding: rate-distortion function and its properties, 
computation of the rate-distortion function, quantization,
lossy source
coding theorem, converse to the coding theorem.
Joint source-channel coding: data processing, separation theorem.
Gaussian channel : capacity of channels with colored Gaussian noise, waterfilling.
Introduction to multi-user communication: networks, broadcast channel, multiple access channel (MAC), channel with states, relay channel, multi-terminal compression.
You should have seen some probability at the level of introduction to stochastic processes or equivalent. For instance, you should be familiar with terms such as i.i.d. random variables, expectation and Gaussian random variables.