Understand, Connect, Apply
Course paths, review material, and focused notes.
Course paths, review material, and focused notes.
Open the corrected bilingual notes online or use the printable PDF.
Open vocabulary readerFrom core principles to data processing and implementation.
Classes, collections, exceptions, file I/O, and modular design.
Cleaning, features, classification, and evaluation.
HDFS, MapReduce, Hive, and HiveQL.
Data structures, computer organization, operating systems, and networks.
Assumptions, objectives, constraints, and limits.
Mean vectors, covariance, and multivariate significance tests.
Hierarchical clustering, K-means, discrimination, and standardization.
Variance, loadings, scores, rotation, and interpretation.
Routing, evolutionary methods, simulated annealing, and tradeoffs.
Sampling, encoding, STFT, spectral features, SNR, and quality.
Equivalent properties, laminate theory, thermal mismatch, FEM, and sensitivity.
Training versus testing, description versus inference, and result limits.
Statistics explains data, optimization guides choices, and computer systems make methods repeatable.