• Genome sequence analysis, hidden Markov models (HMMs), gene-finding algorithms, comparative genomics approaches, RNA sequence analysis, and genome/sequence alignment algorithms.
• Clustering analysis, motif finding, Bayesian networks, small RNAs, microRNAs, and regulatory networks.
• Phylogenomics, comparative population genomics, GWAS, and ancestral genomics.
• Basic programming skills, including command line usage and building pipelines.

Throughout the programme, you will learn about the definition and applications of computer science and programming. You will also receive an overview of hardware, software, operating systems, and the internet. The primary focus will be on the fundamentals of programming, including an introduction to algorithms, pseudocode, programming languages, and programming paradigms.

• What is computer science.
• What is programming.
• Introduction to hardware, software, and operating systems.
• Basics of algorithms.
• Fundamentals of programming.

• Fundamental deep learning concepts.
• How to build a CNN for image classification.
• How to build upon transfer learning.
• How to build and train RNNs and LSTMs.
• How to use deep learning techniques to optimise and accelerate the training of deep models.

• Fundamental data science concepts.
• Exposure to the most popular Python libraries used to tackle data science problems.
• How to work with unclean data.
• How to engineer new features to achieve your goals.
• How to manipulate data and build basic models.

• Genome sequencing technologies and gene annotation.
• Gain familiarity with synteny and the evolution of genomes.
• Explore the genome structures of organisms from various kingdoms.
• Assessment of genomic diversity and its link to phenotypes.
• Differential gene expression analysis and gene regulation.
• Phylogenomics and interpreting phylogenetic trees.
• Basic programming skills, such as command-line usage and building pipelines.
• Running existing data analysis tools using the command line.
• Designing and implementing an automated data analysis pipeline connecting multiple tools.
• Interpreting the results of next-generation sequencing (NGS) data analysis.
• Searching and interpreting genomic information in internet-based databases.

• Understand the basics of machine learning and deep learning.
• Gain familiarity with common techniques used in computer vision.
• Explore convolutional neural networks (CNNs) and their application in image analysis.
• Learn about recurrent neural networks (RNNs) and their role in natural language processing.
• Apply your skills in practical sessions by implementing, training, and testing neural networks, and designing your own models.

• Fundamental machine learning concepts.
• An understanding of various performance metrics for different models and techniques to optimise them.
• How to work with supervised versus unsupervised data.
• How to deal with overfitting and underfitting.
• How to manipulate data and build classical machine learning models.
• Bagging and Boosting techniques.
• Dimensionality reduction.