CSE 5107
Machine Learning
This course is the second course of a two course sequence on machine learning (CSE 417T and CSE517A). It assumes a fundamental understanding of the machine learning foundations (both theoretical and practical) and introduces probabilistic machine learning approaches in-depth as well as advanced topics at the frontier of the field. Topics to be covered include discriminative and generative probabilistic models, kernel methods (e.g., support vector machines, Gaussian processes), neural networks (deep learning), unsupervised learning techniques, and practical machine learning (e.g., feature enginieering, dimensionality reduction, model comparison). Prerequisites: Math 233, CSE 247, ESE 326 or Math 3211, Math 309, and CSE 417T or ESE 417
Instructors
Reviews
8-10 hrs/week
Still pretty theoretical but more applied than 417T which is nice. Neumann can be hit or miss with topics.
5/23/2024
Prof. Neumann is very caring and approachable to the class, and is very helpful, but the problem is that she needs some time to review the materials to understand herself. She sometimes makes mistakes or fails to answer students' questions during lectures. HW, implementation projects and exam is a bit hard, but group projects were pretty easy.
5/14/2022
1. She gives really horrible lectures. She needs to learn math. It's clear that she herself doesn't completely understand the topic. She often gets herself lost in the middle of deductions. 2. CS department should consider inviting some math professors to teach this subject. They are much more consistent, precise, and clear.
9/5/2016
Her lectures were disorganized. She didn't even know what she was talking about.
5/8/2016
I suggest her to watch Andrew ng's videos before teaching this course. Mostly I really don't understand what she is talking, it seems that she just copy all the notes onto the blackboard without explanation, I'm sad I took this course and I spent so much time on that then I realize I should watch Andrew's videos from the very beginning.
4/18/2016
Class is really disorganized, theory and proofs everywhere, I used Andrew Ng's youtube videos to learn this class instead. Avoid her at all costs.
3/6/2016