AI News, regularize
- On Friday, June 8, 2018
- By Read More
In practice, one sets up a fairly general model (such as a neural network or a kernelized support vector machine) and often does as little modeling of the task at hand as possible.
I had a large “ask anything” meeting, I had students coming to office hours, and I had “digital office hours” where the students could ask question via a messenger in a chat room.
They were studying heavy on these exercises, barely using the textbook or their lecture notes to look up theorems or definitions (in other words, some were working “model free” or with a “general purpose model” which says something like “do computations following general rules”).
Also some known problems with machine learning methods can be observed with the students: Students get stuck in local minima (they reach a point where further improvement in impossible by revising the seen data –
Students overfit to the training data (on the test data, aka the exam, they face new problems and oftentimes apply the learned methods to tasks where they don’t work, getting wrong results which would be true if the problem would be a little different).
when the students have more techniques at hand, which is related to a more flexible machine learning method, they do better on unseen data and do not get stuck at bad local minima where no improvement is possible.
Fall 2017: CS/ECE/ME 532 — Matrix Methods in Machine Learning
time: 11-12:15pm Mondays and Wednesdays Office hours: 4-5pm Mondays and 12-1pm Tuesdays when classes are in session in 3537 Engineering Hall TA Office hours: 3-4pm Mondays, 11am-noon Tuesdays, and 2-4pm Fridays in CS 6378 This course is an introduction to machine learning that focuses on matrix methods and
Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning.
The final grade will be allocated to the different components as follows: Letter grades will be assigned using the following hard cutoffs: We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric.
We recommend that you review this exam preparation guide in its entirety and familiarize yourself with the resources on this website before you schedule your exam.
See the Microsoft Certification exam overview for information about registration, videos of typical exam question formats, and other preparation resources.
To help you prepare for this exam, Microsoft recommends that you have hands-on experience with the product and that you use the specified training resources.
- On Friday, February 28, 2020
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