This is an ideal syllabus. If experience be a guide, we shall probably not go so fast nor get to some of the topics at the end.

• LECTURES 1--7: Mathematical Modeling in Population Genetics
  Text: Chapters 1 and 3; review in Chapter 2 as needed.
  Background biology: Mendelian Genetics; genes, alleles, loci, genotypes
  Background probability: Random sampling, law of large numbers, Bernoulli and binomial distributions.
  Population genetics: Random mating models, infinite population models, Hardy-Weinberg equilibrium;
  Wright-Fisher and Moran models; models with selection and adaptation; difference equations.
• LECTURES 8--14: Probabilistic Analysis of DNA sequencing methods
  TEXT: Chapter 4
  Probability: geometric, exponential, Poisson, continuous random variables, law of small numbers, Poisson processes
  Models for coverage analysis (shotgun sequencing, anchors).
  Restriction enzymes, Analysis of restriction enzyme digest libraries
• LECTURES 15--17:Markov chains and applications to population genetics and biological sequences
  Text: Chapter 5
  Markov chains, transition matrices, invariant distributions;
  Markov models for sequences.
  
• LECTURES 18-21: Maximum Likelihood Estimation
  Text: Chapter 6
  Likelihood functions, Maximum likelihood estimation, application to sequence models, and alignment.
• LECTURES 22-25: Dynamic programming and application to sequence alignment.
  Text: Chapter 7
  Elementary theory of dynamic programming, application to global and local alignment of two sequences.
• LECTURES 26-28: Hidden Markov Models
  Text:Chapter 8
  Hidden Markov models, Forward and Viterbi algorithms;
  Application to sequence alignment.