Genomics is a branch of biology where structural and functional knowledge of genomesis studied. This lecture is mainly focused on Big Data challenges in Genomics,application of Artificial Intelligence and Machine Learning in solving such challenges.The amount of data that we are dealing with in genomics is going to reach a zettabyteby 2020, which is much more than the computing power that we are going to have bythen. A normal human contains around 32 billion base pairs, so this means storing thisdata itself is a big challenge let alone analyzing and sequencing the genes. This givesan insight to the depth of the Big Data challenges in the field of genomics and also whyefficient Artificial Intelligence, Machine Learning techniques are needed in this area. Sothis makes it an interesting research direction nowadays.Artificial Intelligence and Machine Learning are used in various aspects of Genomicssuch as sequencing, mapping and analyzing structure of RNA, DNA. Identifying geneticvariants is an interesting use case of machine learning techniques in genomics. Geneticdiseases such as cancer can be cured by properly identifying the genetic variant that iscausing cancer, which helps in creating cancer vaccine that modifies the appropriategenome to cure the disease. Another interesting use case is Next GenerationSequencing (NGS). Next generation sequencing is a technique to sequence entirehuman genome. This enables researchers to study genetics at a level so deep whichwas never achieved before. That is why it is also called deep DNA sequencingtechnology. In this method the challenge is to analyze the very long human genomeinput to find the protein sequence in DNA. Techniques from Artificial Intelligence can beused to efficiently solve this problem.Coming to the achievements that Big Data Analytics brought into this field, the cost ofsequencing came down from $100 Million to $1000 in the span of 15 years from 2000 to2015. Thus there is a lot of research scope in the field of Big Data analytics to solveGenomics.