Week 
Lectures 
Homework 
1 
Introduction
of bioinformatics: data and problems. Part one: combinatorial algorithms for sequence alignment. Sequence alignment: simple edit distance. Pairwise sequence alignment: global and local and variations. 
HW1. Basics of
algorithm analysis and probability 
2 
Scoring
matrices for sequence alignment. The linear space alignment algorithm. The fourRussians trick. 

3 
Techniques
for speeding up sequence alignment. Blast. Multiple sequence alignment: exact, heuristic and approximation algorithms. 
HW2: sequence
alignment. 
4 
Part
two: Hidden Markov Model Probability and probabilistic model: distributions, entropy and sampling. Introduction to statistical inference. 

5 
EM
algorithm. Introduction to hidden Markov model: an motivating problem. What types of problems can HMM solve? 

6 
Algorithms for HMM: forward, backward and
Viterbi. Parameter estimation: the BaumWelch algorithm. 
Programming assignment: implementation of HMM. 
7 
HMM models. Implementation
issues with HMM. HMM for pairwise sequence alignment. 
HW3:
probability and HMM. 
8 
Profile HMM. HMM for multiple sequence alignment. 
Project:
proposal due 
9 
Part
three: phylogenetics. Introduction to phylogenetics. Counting of trees. Parsimony. Perfect phylogeny. 
Project: survey
due. 
10 
Distancebased
tree inference: UPGMA and neighbor joining. Why does neighbor joining work? 

11 
Probability
for phylogenetics. Maximum likelihood inference of phylogeny. 
HW4:
phylogenetics 
12 
Part
four: other topics RNA folding. Protein folding. 
Project: status
report due. 
13 
Genome
rearragenment problems. Algorithms for genome rearrangement problems. 

14 
Course
project presentation. Course project presentation. 

15 
Final exam. 
Project: report
due. 