DOI: 10.1109/CISP-BMEI.2018.8633126

A dataset paper with which auditory info about people talking is collected.

Here are the state-of-the-art as of Laguarta 2021 on the dataset proposed.

CS229: instead of solving a problem, learn from data to find a model to solve the problem approximately.

CS109: machine learning is the act of using some input to come up with some prediction, where the model is parameterized via a bunch of parameters. Hence, parameter learning approaches is how machine learning works.

CS205L: training data + model to estimate new data points with minimal error; the parallel is a “knowledge based system” with interpolation

https://cs229.stanford.edu/

Course Project

• Deliverables: proposal (300-500 words), milestone (3 pages), final report (5 pages), poster
• Evaluation: technical quality, originality, community

Lectures

Basics + Linear Methods

• SU-CS229 JAN062025
• SU-CS229 JAN082025

For multi-class classification, the macroaverage is the average of statistical values (prec, recc, etc.) after they have been computed for each seperate class.

The microaverage is the combination of a confusion matrix BEFORE statistical values are computed.

For a charge to do something in a magnetic field, it has to have velocity; nothing happens without movement.

So:

\begin{equation} \vec{F}_{M} = q \vec{v} \times \vec{B} \end{equation}

To calculate: magnitude: \(qvB \sin \theta\) + right hand rule.

Radius

You maybe asked to find the radius of the path the particle takes, so:

\begin{equation} \frac{v^{2}}{r} = a \end{equation}

So, the net force here is:

\begin{equation} qvB = Ma \end{equation}

So plug in and solve