Speaker: Alicia Wei

Title: Introduction to Data Science Workflow

Description: Learn about the most common machine learning algorithms in regression, classification, and clustering problems which include linear regression, logistic regression, naive Bayes, support vector machine, and k-means. Then an introduction to the Python code for a classic classification problem, Titanic survival, and an overview through preprocessing, exploratory data analysis, visualization, model architecture, and evaluating performance on test data.

Speaker: David Li

Title: Introduction of ML models in Data science

Description: Introduction of one of the most wide-used categories of models-supervised learning. Detailed descriptions of the methods, application, and characteristics of several models. A coding example also provided for the audience to be familiar with how to prepare data and construct a model using sklearn and pandas.