The document provides details on a course calendar and lecture plan for hidden Markov models (HMM). 1) The course calendar covers topics like Bayesian estimation, Kalman filters, particle filters, hidden Markov models, supervised learning, and clustering algorithms over 14 weeks. 2) The HMM lecture plan introduces discrete-time HMMs and their applications. It covers the three main problems of HMMs - evaluation, decoding, and learning. Evaluation calculates the probability of an output sequence, decoding finds the most probable hidden state sequence, and learning estimates model parameters from training data. 3) The trellis diagram and forward algorithm are described for solving the evaluation problem, while the Viterbi and forward-backward algorithms are mentioned