Fall 2019 CSE4095 Introduction to Machine Learning

The objective of the course is to introduce basic concepts in machine learning to undergraduate students, and enable them to use machine learning methods and algorithms in real-life applications. This course covers basic topics of machine learning, such as supervised learning (logistic regression and naive bayes for classification, linear and polynomial regression, support vector machine, etc), unsupervised learning (K-means clustering) and some advanced topics, such as kernel and deep learning. Because of the diversity in machine learning topics, the materials covered in this course may vary among semesters. The course consists of lectures, programming-involving and non-programming assignments and exams. Lectures will serve as the vehicle for the instructor to introduce concepts and knowledge to students. Exams are used to test if certain basic concepts have been mastered. Programming-involving assignments will be used for students to get profound hands-on experience by programming certain machine learning algorithms identified from the recent literature.

Spr 2019 CSE4095 Introduction to Machine Learning

Spr 2019 CSE5095/SE5201 Embedded/Networked Systems Modeling Abstractions

Fall 2017 CSE4095 Introduction to Machine Learning

Spr 2018 CSE5095/SE5301 Embedded/Networked Systems Modeling Abstractions

Teaching Assistant

Spring 2012 ESE605 Convex Optimization (Upenn)

Fall 2012 ESE504 Introduction to Optimization Theory (Upenn)