Schedule

NOTE: ID/PWD to access course materials will be distributed in the first class

 Week Date  Contents  Reading List  Download Assigned  DUE
 1 8/26
Logistics

 logistics hw0  
  8/28Introduction to MLDM4 Ch 1, CIML Ch 1.2, MMDS Ch 1intro, lab-personality  hw0
 2 9/2
NO CLASS (Labor Day)



  9/4Inputs and Outputs: nominal, ordinal, interval, ratioDM4 Ch 2-3, IDM2 Ch 2.1
in-outlab-iris

 3 9/9Jupyter Notebook LabWeka, Jupyterlab-jupyterlab-heart-disease (azure)

  9/11Distance: euclidean, minkowski, mahalanobis, mutual informationIDM2 Ch 2.4similarityhw1
 4 9/16Decision tree: hunt's algorithm, gini index, entropy, information gain
CIML Ch 1, IDM2 Ch 3.3, DM4 Ch 4.3
decision-tree

  9/181R and Naive Bayes ClassificationCIML Ch 9.3, IDM2 Ch 4.4, DM4 Ch 4.2
proj1
hw1 (9/22)
 5 9/23Evaluation: confidence interval, cross-validation, bootstraping, hyperparameter, model comparisonDM4 Ch 5, CIML Ch 2.5-2.7, Ch 5.5-5.7


  9/25Evaluation: loss function, cost-sensitive learning, confusion matrix, various error measuresDM4 Ch 5, CIML Ch 1.4
hw2
 6 9/30Limits of Learning: underfitting, overfittingCIML Ch 2.4, IDM2 Ch 3.4


  10/2Association rules: apriori, fp-growth DM4 Ch 4, IDM2 Ch 5


hw2 (10/6)
 7 10/7Weka & Jupyter Notebook Lab



  10/9Linear Models


proj1 (10/13)
 8 10/14Linear Models: regression, perceptron, winnowDM4 Ch 4, CIML Ch 4, MMDS Ch 12.2



  10/16 MIDTERM



 9 10/21Clustering: partitional vs. hierarchical, k-means vs. agglomerative, inter-cluster distances, incrementalDM4 Ch 7, CIML Ch 3, MMDS Ch 7
proj2
  10/23Project #2 Discussion



 10 10/28Support Vector Machine (SVM): support vectors, hyperplane, margin, slack penalty, hinge loss
DM4 Ch 7, CIML Ch 7.7, MMDS Ch 12.3



  10/30Support Vector Machine (SVM): gradient decent, SGD, kernels, SMO (weka)
DM4 Ch 7, CIML Ch 11.5-11.6, MMDS Ch 12.3



 11 11/4Project #2 Discussion



  11/6Feature Selection: filter, wrapper, embedded, forward stepwise, backward elimination, hill-climbing
DM4 Ch 8, CIML Ch 5.1-5.4



 12 11/11Dimensionality Reduction: PCA, SVDDM4 Ch 8, CIML Ch 3.5, Ch MMDS 11


  11/13Ensemble Learning: bagging, randomization, boostingDM4 Ch 12, CIML Ch 13, IDM2 Ch 4.10



 13 11/18Deep LearningIDM2 Ch 4.7-4.8, DM4 Ch 10


  11/20Deep Learning
IDM2 Ch 4.7-4.8, DM4 Ch 10


 14 11/25
NO CLASS (Thanksgiving Holiday)



  11/27NO CLASS (Thanksgiving Holiday)



 15 12/2Project #2 Discussion



  12/4ML applications
DM4 Ch 12, CIML Ch 2.7


proj2
 16 12/9Presentation




  12/11Presentation




 17
 FINAL EXAM