The Foundation of Applied Machine Learning (Summer 2019)
Online/in-Person Graduate course, University of California, Riverside, Department of Physics and Astronomy, 2019
This is the webpage for “The foundation of applied machine learning” for Summer 2019 by Prof. Bahram Mobasher. You can find the related materials (homeworks, codes, jupyter notebooks, …) under the link. You can get/clone the repository of this course from my github page as well.
What is this course about?
Machine Learning is about building automated methods that improve their own performance through learning patterns in data, and then use the uncovered patterns, which acts as our theoretical model, to predict the future and make decisions. Examples include document, image, and handwriting classification, spam filtering, face/speech recognition, decision making, automated navigation to name a few. This course covers the theory and practical algorithms for machine learning from a variety of perspectives.
Video lectures for online students:
Videos are accessible in the iLearn under section(002)
Syllabus of the course:
Courses:
Week 6: Machine Learning: Regression, Gradient Descent, Decision Trees
Week 7: Machine Learning: Decision Trees, Random Forests, Support Vector Machine
Lectures:
Lecture 1 Lecture 2 Lecture 3 Lecture 4 Lecture 5 Lecture 6
Lecture 7 Lecture 8 Lecture 9 Lecture 10 Lecture 11 Lecture 12
Lecture 13 Lecture 14 Lecture 15 Lecture 16 Lecture 17 Lecture 18