Spring 2024
EE 425X: Machine Learning: A Signal
Processing Perspective
- Some course material is borrowed from cs229 of
Stanford
- Prerequisites: MATH 207, STAT/EE 322 or
equivalent
- Location, Time: 11-12 M-W-F,
1016 Coover (2042 Coover on some Fridays)
- Instructor: Prof Namrata Vaswani
- Office
Hours: TBD
- Office: 3121 Coover Hall
- Email: namrata
AT iastate DOT edu Phone: 515-294-4012
- Grading policy
- Homeworks, Final
exam, Class participation. Details: see Canvas
- Syllabus:
- Background material:
- probability
- calculus
- linear algebra
- python
- Supervised
Learning
- Linear
Regression
- Logistic
Regression
- Generative
Algorithms (Gaussian & discrete-valued case; Naive Bayes assumption)
- Support
Vector Machines
- Unsupervised
Learning
- Introduction
to Deep Learning / Neural Networks
- MLP,
CNN,
- hands-on
intro to some other modern architectures
- Model-based
Learning
- Least Squares
estimation –
- used also
in LinReg training
- Use of
sparsity as a regularizer
- Introduction
to use of sparsity and low-rank assumptions in various modern ML
problems
- Learning
Theory and Bias-Variance Tradeoff
-
·
Access Statement for Students
with Documented Disabilities:
o Iowa
State University is committed to assuring that all educational activities are
free from discrimination and harassment based on disability status. Students
requesting accommodations for a documented disability are required to meet with
staff in Student Accessibility Services (SAS) to establish eligibility and
learn about related processes. Eligible students will be provided with a
Notification Letter for each course and reasonable accommodations will be
arranged after timely delivery of the Notification Letter to the instructor.
Students are encouraged to deliver Notification Letters as early in the
semester as possible. SAS, a unit in the Dean of Students Office, is located in
room 1076 Student Services Building or online at www.sas.dso.iastate.edu.
Contact SAS by email at accessibility@iastate.edu or by phone at 515-294-7220
for additional information. Since this is a small class, I am happy to also
provide other options. Please do not hesitate to discuss your needs with me.
·
Project Details
o See Canvas
- Course
Material
- Python
help
- Linear
Algebra
- Probability
- Machine
Learning course notes
- Theory:
-
- Topics
from Estimation/Detection
Theory course (EE 527):
- Least
Squares estimation
- Bayesian
Estimation
- MMSE,
linear MMSE estimation and Kalman filtering
- Hidden
Markov Models (HMM)