This lecture discusses molecular evolution and phylogenetics, focusing on methods to infer evolutionary relationships among species using DNA sequence data. Key points include the role of natural selection in shaping species divergence, the use of genomic data to study evolutionary relatedness, and the development of gene and species trees. The lecture introduces two probabilistic models of divergence: Jukes-Cantor and Kimura. It also covers algorithmic approaches like UPGMA and Neighbor-Joining for building phylogenetic trees. The lecture highlights the importance of tree structures in representing evolutionary relationships, noting that branch lengths can represent genetic change or time. It discusses the distinction between gene and species trees, and the complexities of inferring phylogenies, including issues like back mutations and homoplasy. The lecture also addresses the advantages of using genomic data, such as the vast amount of information available, and the challenges in interpreting traits. It introduces three types of trees: cladograms, phylograms, and ultrametric trees, each with different interpretations of branch lengths. The lecture explains how distance matrices can be used to construct trees, and discusses tree-building algorithms like UPGMA, Neighbor-Joining, and Parsimony. Maximum likelihood methods are also introduced as a statistical approach to phylogenetic inference. The lecture emphasizes the importance of choosing appropriate optimality criteria and algorithms to accurately infer evolutionary relationships.This lecture discusses molecular evolution and phylogenetics, focusing on methods to infer evolutionary relationships among species using DNA sequence data. Key points include the role of natural selection in shaping species divergence, the use of genomic data to study evolutionary relatedness, and the development of gene and species trees. The lecture introduces two probabilistic models of divergence: Jukes-Cantor and Kimura. It also covers algorithmic approaches like UPGMA and Neighbor-Joining for building phylogenetic trees. The lecture highlights the importance of tree structures in representing evolutionary relationships, noting that branch lengths can represent genetic change or time. It discusses the distinction between gene and species trees, and the complexities of inferring phylogenies, including issues like back mutations and homoplasy. The lecture also addresses the advantages of using genomic data, such as the vast amount of information available, and the challenges in interpreting traits. It introduces three types of trees: cladograms, phylograms, and ultrametric trees, each with different interpretations of branch lengths. The lecture explains how distance matrices can be used to construct trees, and discusses tree-building algorithms like UPGMA, Neighbor-Joining, and Parsimony. Maximum likelihood methods are also introduced as a statistical approach to phylogenetic inference. The lecture emphasizes the importance of choosing appropriate optimality criteria and algorithms to accurately infer evolutionary relationships.