This course introduces data mining and machine learning algorithms for analyzing big data, with a focus on various types of algorithms that can handle large-scale data. The course consists of seven chapters: Introduction to MapReduce and Spark, Introduction to Locally Sensitive Hashing Algorithms, Large Scale Machine Learning Algorithms, Data and Matrix Dimensionality Reduction, Graph Algorithms and Community Discovery, Introduction to Recommender Systems, and Temporal and Spatio-Temporal Data Mining. Big data algorithms and analytics refer to the methods or tools used to collect, process, and analyze data from different large data sets. These datasets may come from a variety of sources such as the Web, mobile applications, email, social media, and connected smart devices. They typically represent data generated at high speed in a variety of forms, ranging from structured (database tables, Excel sheets) to semi-structured (XML files, web pages) to unstructured (images, audio files). Therefore, this course covers most of the data types likely to be encountered in Big Data processing tasks in seven chapters, helping students understand how to use algorithms wisely to solve large-scale data problems. 