Carnegie Mellon University

Electrical and Computer Engineering

College of Engineering

Course Information

18-785RW: Data, Inference, and Applied Machine Learning




Please see the ECE website for more information. This course will provide the methods and skills required to utilize data and quantitative models to automate predictive analytics and make improved decisions. From descriptive statistics to data analysis to machine learning the course will demonstrate the process of collecting, cleaning, interpreting, transforming, exploring, analyzing and modeling data with the goal of extracting information, communicating insights and supporting decision-making. The advantages and disadvantages of linear, nonlinear, parametric, nonparametric and ensemble methods will be discussed while exploring the challenges of both supervised and unsupervised learning. The importance of quantifying uncertainty, statistical hypothesis testing and communicating confidence in model results will be emphasized. The advantages of using visualization techniques to explore the data and communicate the outcomes will be highlighted throughout. Applications will include visualization, clustering, ranking, pattern recognition, anomaly detection, data mining, classification, regression, forecasting and risk analysis. Participants will obtain hands-on experience during project assignments that utilize publicly available datasets and address practical challenges.

In Fall 2019 this course is broadcast from the CMU-Africa campus. ECE Pittsburgh students attend classes synchronously with students in Rwanda.

Last Modified: 2023-04-14 10:27AM

Semesters offered:

  • Fall 2023
  • Fall 2020
  • Fall 2019
  • Fall 2018
  • Fall 2017