Multivariate Statistics and Materials Informatics
Last updated:
- November 21, 2024 - Posted link to Module 3 Gateway Quiz, updated due date.
- November 12, 2024 - Posted Homework 2-3. Updated Lecture Schedule
- October 7, 2024 - Added Module 2 Gateway Quiz under “Assessment”
- September 26, 2024 - Changed date for Module 2 Gateway Quiz
- September 13, 2024 — Added a new homework problem to HW1.
- September 12, 2024 — Updated use of AI policy.
- September 12, 2024 — Added in-lecture notebook for Module 1, Lecture 6.
- September 10, 2024 — Added new homework problems to HW1. Updated HW1 due date.
- September 9, 2024 — Added new homework problems to HW1
- September 5, 2024 — Updated the lecture schedule
Instructor
Prof. Kristofer Reyes
Course time and location
- Tuesdays and Thursdays, 3:30 - 4:50 PM
- Baldy 127
Assessment
The course bases grades on four homework sets (100-150 points each) and five in-class quizzes (50 points each).
Prerequisites
Textbook
There is no official textbook. We will cover all requisite material in lectures, homework, and take-home optional labs. Additional good reference material is listed below. Many are available online:
- “Introduction to Probability Models,” Ross
- “Introduction to Linear Algebra,” Strang
- “Pattern Recognition and Machine Learning,” Bishop
- “The Elements of Statistical Learning,” Hastie, Tibshirani, Friedman
- “All of Statistics,” Wasserman
- Python programming tutorials at “https://diveintopython.org”
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Description
This course introduces students to fundamental techniques, models, and ideas from statistics, machine learning, data science, and applied mathematics that underpin the new field of materials informatics. By the end of the course, students will be able to:
- Programmatically retrieve and manipulate data sets from a variety of sources.
- Perform exploratory data analysis and visualization to understand the data’s properties, trends, and geometry.