Subject:
Computer Science (CS)
Catalog number:
685
Unit weight:
0.50
Meet type:
LEC
Grading basis:
NUM
Cross-listing(s):
N/A
Requisites:
N/A
Description:
Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithm paradigms for such tasks. Central concepts: VC-dimension, Margins of classifier, Sparsity and description length. Performance guarantees: Generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: Neural networks, Kernel methods and Support Vector machines, Applications to Data Mining.
Topic titles:
N/A
Faculty:
Mathematics (MAT)
Academic level:
GRD
Course ID:
000624
