PHYS360  STATS, DATA MINING, MACH
This is a practical course on computing, numerical methods, statistics, and data analysis techniques with particular emphasis on data mining and machine learning applied to large datasets. Topics include basic numerical methods and algorithms, probability theory, classical and Bayesian statistical inference, model fitting, Monte Carlo methods, and classification. We will be using Python for the exercises. Prior experience in programming (in any language) is required.
Section 001  LEC
MW 0100PM0230PM
R 0400PM0500PM
R 0400PM0500PM
SAKO, MASAO
DAVID RITTENHOUSE LAB 3N1H
DAVID RITTENHOUSE LAB 3N1H
DAVID RITTENHOUSE LAB 3N1H
Events

Department Colloquium: "Selfdriven phase transitions in living matter"
January 23, 2019  4:00 pm
Joshua Shaevitz, LewisSigler Institute, Princeton University
David Rittenhouse Laboratory, A8

Physics and Astronomy Postponed Exams
January 24, 2019  6:00 pm
DRL, Room A8

Condensed Matter seminar: "The Berry curvature dipole of metals and the crossover from composite fermions to exciton superfluid”
January 25, 2019  2:00 pm
Inti SodemannVilladiego, Max Planck Institute, Dresden
David Rittenhouse Laboratory, A4