12 Useful Things to Know About Machine Learning - Apr 12, 2018.
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions.
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Causation, Correlation, Feature Engineering, High-dimensional, Machine Learning, Overfitting, Pedro Domingos
- Must-Know: What is the curse of dimensionality? - Apr 18, 2017.
What is the curse of dimensionality? This post gives a no-nonsense overview of the concept, plain and simple.
Dimensionality Reduction, High-dimensional, Interview Questions
- 17 More Must-Know Data Science Interview Questions and Answers, Part 2 - Feb 22, 2017.
The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.
Algorithms, Data Science, Ensemble Methods, Feature Engineering, Feature Selection, High-dimensional, Interview Questions, Overfitting, Unsupervised Learning
- Seven Techniques for Data Dimensionality Reduction - May 14, 2015.
Performing data mining with high dimensional data sets. Comparative study of different feature selection techniques like Missing Values Ratio, Low Variance Filter, PCA, Random Forests / Ensemble Trees etc.
Data Processing, High-dimensional, Knime, Rosaria Silipo
- Interview: Arijit Sengupta, CEO, BeyondCore on Advanced Analytics and Big Data - May 9, 2014.
We discuss traditional analytics vs. modern analytics, avoiding over-simplification, human-technology interaction for Big Data, challenges in democratizing analytics and more.
Advanced Analytics, Arijit Sengupta, BeyondCore, High-dimensional, Interview
- MMDS 2014: Workshop on Algorithms for Modern Massive Data Sets, Berkeley, June 2014 - Mar 25, 2014.
The MMDS 2014 will address algorithmic, mathematical, and statistical challenges in modern statistical data analysis. Registration is open and you can apply to present a poster.
Algorithms, Berkeley-CA, High-dimensional, Massive Datasets, Poster, Workshop
