This lecture covers the concept of convergence in distribution, where Xn converges to X if the cumulative distribution functions converge. Various propositions and remarks are discussed, highlighting the conditions for convergence and the characterization of convergence using moments. The lecture concludes with the characterization of convergence with moments and the heuristics behind it.