Concepts of Machine Learning

CT Score

1500

#0 of 0 in IS

Difficulty

—/10

Workload

—h/wk

Median grade

from grade distribution

Would take again

—

n = 0 verified

ESTIMATEStarted from public course data. The first verified student reviews replace these numbers.

Grade distribution

Student-reported · course feedback surveys

54.7%

16.6%

8.7%

5.8%

2.7%

1.2%

0.2%

0.7%

0.2%

0.5%

0.2%

0.5%

A+

A-

B+

B-

C+

C-

D+

D-

By professor: same course, different game

PROFESSORSCOREGRADEHRS/WKTERMS

Professor ratings →

Sections

No section data for this term yet.

In the tree

Statistical tools that are fundamental for financial modeling, analyzing financial data and further studies in financial engineering. Topics include summary statistics, statistical plots, point estimation, accuracy and precision, confidence interval, Monte Carlo simulation, maximum likelihood estimation, normal mixture, resampling, hypothesis testing, simple linear regression, multiple linear regression, variable selection, regression diagnostics, autocorrelation, moving average models, filtering, autoregressive models, ARIMA, forecasting, and selected additional topics. Implementations are done using R. Course Information: Credit is not given toward graduation for: Credit is not given for both IE 522 and SE 524 . Prerequisite: IE 300 and MATH 461 . Restricted to Sophomore, Junior, or Senior standing→IS 327→unlocks 0 courses

No mapped courses require this one yet. See on the map →

Study groups

0 active

🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.