After completing this course, you will be able to:

• Represent uncertainty in parameters in engineering or scientific models using probability theory
• Propagate uncertainty through physical models to quantify the induced uncertainty in quantities of interest
• Solve basic supervised learning tasks, such as: regression, classification, and filtering
• Solve basic unsupervised learning tasks, such as: clustering, dimensionality reduction, and density estimation
• Create new models that encode physical information and other causal assumptions
• Calibrate arbitrary models using data
• Apply various Python coding skills
• Load and visualize data sets in Jupyter notebooks
• Visualize uncertainty in Jupyter notebooks
• Recognize basic Python software (e.g., Pandas, numpy, scipy, scikit-learn) and advanced Python software (e.g., pymc3, pytorch, pyrho, Tensorflow) commonly used in data analytics

Section 1: Introduction

• Introduction to Predictive Modeling

Section 2: Review of Probability Theory

• Basics of Probability Theory
• Discrete Random Variables
• Continuous Random Variables
• Collections of Random Variables
• Random Vectors

Section 3: Uncertainty Propagation

• Basic Sampling
• The Monte Carlo Method for Estimating Expectations
• Monte Carlo Estimates of Various Statistics
• Quantify Uncertainty in Monte Carlo Estimates

Section 4: Principles of Bayesian Inference

• Selecting Prior Information

• Analytical Examples of Bayesian Inference

Section 5: Supervised Learning: Linear Regression and Logistic Regression

• Linear Regression Via Least Squares
• Bayesian Linear Regression
• Advanced Topics in Bayesian Linear Regression
• Classification

Section 6: Unsupervised Learning

• Clustering and Density Estimation
• Dimensionality Reduction

Section 7: State-Space Models

• State-Space Models – Filtering Basics
• State-Space Models – Kalman Filters

Section 8: Gaussian Process Regression

• Gaussian Process Regression – Priors on Function Spaces
• Gaussian Process Regression – Conditioning on Data
• Bayesian Global Optimization

Section 9: Neural Networks

• Deep Neural Networks
• Deep Neural Networks Continued
• Physics-Informed Deep Neural Networks

Section 10: Advanced Methods for Characterizing Posteriors

• Sampling Methods
• Variational Inference

• Hands-on activities were fun and good way of teaching concepts.

• Prof Bilionis clearly explains material. The lectures material is very useful for us to understand how to apply data analysis technique using build in packages.

• I appreciate the professor in creating such intense materials for the coursework.

• Overall this class is a great addition to the ME curriculum. I wish that more courses were delivered in this method since it makes it very easy to fit this course in around an active research schedule. Specific qualities that are excellent: The edX implementation is excellent Feedback on Piazza helps more students than just the ones who actually asked the questions HW problems that were more like projects really helped to develop understanding Hands-on problem demonstrations helped clarify minor coding difficulty. No exams is excellent and enables focus on more relevant skills than pure memorization.

• Professor Billionis did a great job explaining the concepts of the class. Also, he understands that students are struggling during this time and designed the class material for it. I can tell this class was the best one I have had online during the pandemic.

• I thought the course was a lot of work, but I enjoyed it in the end, and I think I learned a lot. I liked the asynchronous format that allowed me to re-watch the lectures several times and complete the material when it was convenient. I liked that the hands-on activities gave good examples of how to call the functions we used. The homework was quite practical. As a result of the course, I have the sense that I would know the right place to start on a real data science problem.

• The instructor has structured the course really well. The questions to be answered are listed/explained during the beginning of the lecture series, following which the questions are answered during the lectures itself.

• Excellent mathematical foundations to understand the real application of data analytics

• I found this course extremely helpful, and gives a very complete and overall overview of the state-of-the-art machine learning techniques used for various applications. The hands-on activities were brilliantly designed in a way that the students are kept motivated to complete them. The coding snippets in the activities are very helpful to develop efficient coding skills when dealing with very big data. I am thoroughly satisfied with the course and will recommend this to students.

• I believe this was one of my favorite courses I have taken at Purdue University. This class was challenging, but doable. I learned so much about uncertainty analysis, which is directly pertinent to my research. I would say Dr. Bilionis truly cared about what we learned, and wanted us to grasp it on a fundamental level. Though I still have much more to learn, I have a great foundation. I wish Dr. Bilionis would continue to create such in depth lectures and notes. The aid of the jupyter notebooks with the lectures allowed for me to easily follow what I just learned as well as apply it. I also learned much more Python than expected from this class, and I have become a better programmer than before.

• I enjoyed course layout, with the lectures, reading material with references, and the hands on activities coupled with the homework. The notebook activities were really excellent.

• I think this was a good course for an introduction to machine learning. I also like that the course was mostly application-based in the homework assignments. I mostly deal with the theoretical aspects, but being able to apply them in Python helped me a lot. The material was organized very effectively. The Piazza page is also great. I can always look there for help or if I'm confused with something.