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(Hyper)parameter tuning
 

Classification
 

Deep learning
 

Feature Selection, Dimensionality Reduction, and Anomaly Detection
 

Gradient Descent Methods
 

Linear methods
 

Naıve Bayes
 

Parameter tunning
 

Software
Deep learning, Support Vector Machines (SVM), Toolkits/Frameworks...

Support vector machines
 

Trees & Forests
 

Tutorials
 

Unsupervised/Clustering
 

  


Abridged List of Machine Learning Topics  
An Introduction to Conditional Random Fields  Charles Sutton, Andrew McCallum
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data  John Lafferty, Andrew McCallum, Fernando Pereira
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?  Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim
Machine Learning in Automated Text Categorization  F Sebastiani - A must read paper.
Machine Learning: The High-Interest Credit Card of Technical Debt  D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young - See also an overview of the paper in the blog post.
MLcomp   - MLcomp is a free website for objectively comparing machine learning programs across various datasets for multiple problem domains.
Reliable Reasoning: Induction and Statistical Learning Theory  Gilbert Harman, Sanjeev Kulkarni
Scaling to very very large corpora for natural language disambiguation  Michele Banko, Eric Brill
Statistical Machine Translation   - The website is dedicated to research in statistical machine translation, i.e. the translation of text from one human language to another by a computer that learned how to translate from vast amounts of translated text.
t-Distributed Stochastic Neighbor Embedding (t-SNE)   - a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction.  Trevor Hastie , Robert Tibshirani , Jerome Friedman