CSE 426 / 326: Fundamentals of Machine Learning

Description: An introductory course offers a broad overview of the main techniques in machine learning. Students will study the basic concepts of advanced machine learning methods as well as their theoretical background. Topics of learning theory (bias /variance tradeoffs; VC theory); supervised learning parametric / nonparametric methods, Bayesian models, support vector machines, neural networks); unsupervised learning (dimensionality reduction, kernel tricks, clustering) and reinforcement learning will be covered.

• Class meetings: MW 2:35pm-3:50pm @ Neville Hall 003

• Instructor: Miaomiao Zhang (miaomiao -at- cse.lehigh.edu)

• Office hours: MW 4:00pm-5:00pm @ PL514A

• Teaching Assistant: Yuan Xue (yux715-at-lehigh.edu), TTh 9:00am-11:00am @ PL114, or by appointment

Lecture materials including slides and notes will be posted on CourseSite

Prerequisites by Topic

• Linear algebra: matrices, vectors, vector spaces, basic matrix transformations, eigenvalues and linear differential equations

• Probability and statistics: Distribution of random variables, Bayesian rules, random sampling, hypotheses testing, correlation and regression

• Programming: algorithm and implementation in Matlab, or Python, or C/C++

Textbooks

Shai Shalev-Shwartz and Shai Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, 1st Edition, Cambridge University Press, 2014, ISBN 978-1107057135

Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar, “Foundations of Machine Learning (Adaptive Computation and Machine Learning series)”, The MIT Press, 2012, ISBN 978-0262018258

Christopher Bishop, "Pattern Recognition and Machine Learning”, ISBN 978-0-387-31073-2

Grading

• Midterm (15%)

• Problem sets and projects (65%)

• Final (15%)

• Participation (5%)

All programming will be in Matlab, a powerful, free programming tool for Lehigh students: https://software.lehigh.edu/install/. Reports must be written in LaTeX and submitted as PDF.

Homeworks are due by midnight (11:59:59 PM) on the due date. Late penalty is 15% per day. Late assignments after three days will not be accepted.

Cheating

Discussions with classmates are strongly encouraged. However, any assignment must be your own work based on your own understanding of the questions. Both copying others’ solution and let others copy your solution will be considered as cheating.

You must work on exams within limited time independently. Students caught cheating will fail this course. Please don't take that chance.

Schedule

Disclaimer

The instructor reserves the right to make changes to the course schedule, syllabus, and project deadlines. Changes will be announced early in advance.