This course provides an introduction to Data Science and Machine Learning focusing on the practical application of models to real-world supervised and unsupervised learning problems. We will discuss methods for linear regression, classification, and clustering, and apply them to perform sentiment analysis, implement a recommendation system, and perform image classification or gesture recognition. One of the main objectives of the course is to become familiar with the data science workflow, starting from posing a problem, understanding and preparing the data, training and evaluating a model, up to presenting and interpreting its results. We will also touch upon concepts such as similarity-based learning, feature engineering, data manipulation, and visualization. The course uses Python, which is currently the most popular programming language for data science.
Prerequisites: CSE131, MATH233, CSE247 (can be taken concurrently)