Data Inference and Applied Machine Learning
This course will provide the methods and skills required for utilizing 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. The full course syllabus contains additional information and can be found in PDF format HERE. Assignments rely on accessing data via APIs from public sources and stock market data from Alpha Vantage https://www.alphavantage.co.
There will be no required textbooks, though we suggest the following to help you to study (all available online):
The course Academic Integrity Policy must be followed when doing assignments and on the message boards at all times. Details on ECE's Academic Integrity Policy can be found in the course syllabus and HERE.