Schedule of Classes

• Lecture 1, Jan 26: Overview of Machine Learning Paradigms, Model Selection, Generalization, Over-fitting, Decision Theory
  Slides on course organization
  Slides on: Introduction to Machine Learning, Model Selection, Over-fitting, Decision theory.
  

• Lecture 2, Feb 2: Classification, Bayesian classifier, KNN methods, bias-variance tradeoff, Curse of Dimensionality, Dimensionality reduction
  Slides on classification
  Slides on PCA - part 1
  Slides on PCA - part 2
  Application of PCA
  Slides on LDA
  Chapter on classification
  

• Lecture 3, Feb 9: Introduction to ensembles of classifiers; Bagging; Pasting Small Votes; Boosting
  Slides
  Ensemble Learning
  Ensemble Based Systems in Decision Making
  Bagging Predictors
  

• Lecture 4, Feb 16: Why Bagging works; comparisons between Bagging and Boosting
  Bagging, Boosting, and C4.5
  

• Lecture 5, Feb 23: Clustering and Clustering Ensembles
  Slides on Clustering
  Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions
  Slides on Cluster Ensembles
  

• Lecture 6, Mar 2: More on clustering ensembles. Discussion of ideas for projects.

• Lecture 7, Mar 16: Students' presentations:
  Ali Soleimani: Moderate diversity for better cluster ensembles
  Sharath Hiremagalore: Casting out Demons: Sanitizing Training Data for Anomaly Sensors Presentation
  

• Lecture 8, Mar 23: Students' presentation:
  Suchet Chachra: PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection
  Project Proposal DUE.
  Guidelines on the project and on the project term paper
  

• Lecture 9, Mar 30: Suchet will continue the presentation of the PNrule paper.
  I will present the paper on Meta Clustering Presentation
  

• Lecture 10, Apr 6: We'll continue the discussion on Meta Clustering.
  We'll discuss the paper On error correlation and accuracy of nearest neighbor ensemble classifiers Presentation
  
  

• Lecture 11, Apr 13:
  Ali Soleimani: Cluster Ensemble Selection
  Sharath Hiremagalore: Diversity-based Weighting Schemes for Clustering Ensembles
  

• Lecture 12, Apr 20:
  Sharath will continue the discussion on the Weighting Clustering Ensembles paper.
  Suchet Chachra: SemiBoost: Boosting for Semi-supervised Learning
  

• Lecture 13 , Apr 27: Guest lectures:
  Roberto Avogadri: Fuzzy ensemble clustering based on random projections for DNA microarray data analysis.
  Francesco Gullo: An Information-Theoretic Approach to Hierarchical Clustering of Uncertain Data.
  

• Lecture 14 , May 5: Project presentations.
  Ali Soleimani.

• May 11: Project presentations.
  Suchet Chachra
  Sharath Hiremagalore
  Final report due.