Contrastive learning (CL) has exploded in popularity due to its ability to learn effective represen- tations using vast quantities of unlabelled data across multiple domains. CL underlies some of the most impressive applications of generative AI for the general public. We will review the fundamen- tals and applied work on contrastive learning representations focusing on three main topics 1) CL in supervised, unsupervised and self-supervised setup and its revival in AI research as an instance discriminator. In this part, we will focus on learning about the nuts and bolts, such as different augmentation techniques, loss functions, performance evaluation metrics, and some theoretical un- derstanding of contrastive loss. We will also present the methods supporting DALL·E 2, a popular generative AI. 2) Learning contrastive representations across vision, text, time series, tabular data and knowledge graph modalities. Specifically, we will present the literature representative of so- lution approaches regarding new augmentation techniques, modification in the loss function, and additional information. This part will also have a small hands-on session on some of the methods learned. 3) Discussing the various theoretical and empirical claims for CL’s success, including the role of negative examples. We will also present some work that challenges the shared information assumption of CL and propose alternative explanations. Finally, we will conclude with some future directions and applications for CL.