Statistical Learning Using R
This course aims to make participants proficient in the fundamentals of statistics learning, which brings together the fields of machine learning and statistics. Participants will learn how to use an array of computer-based techniques using the programming language R to make statistical predictions and inference on large data sets.
Classes will be a mix of presentations to convey concepts, working through examples, and group exercises. Students will be randomly assembled each week into groups of 2 or 3, and must complete a class assignment based on the material covered. In addition, participants must take an individual quiz at the start of each class about the materials covered the previous day. All instruction and materials will be in English. The teacher for this course is Dr. Tim Matis.
Attendance is mandatory. Any student who does not attend at least 90% of the meetings will automatically fail the course. The participant’s grade will be based on in-class group work (60%) and individual quizzes (40%).
The software used in this class are free for personal use.
Dr. Timothy Matis has a Ph.D in Philosophy in Industrial Engineering, and master's and bachelor's degree of Science in Industrial Engineering. He is currently a professor at Texas Tech University and provides periodic research and teaching assistance to the School of Engineering at the Pontífica Universidad Católica de Valparaiso as a visiting professor.