Schedule

lab2NOTE: 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 1, CIML 1.2, MMDS 1intro, lab-personality  hw0
 2 9/2
NO CLASS (Labor Day)



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

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

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

  9/181R and Naive Bayes ClassificationIDM2 4.4, DM4 4.2, CIML 9.3naive-bayesproj1
hw1-sol (9/22)
 5 9/23Project #1 Hands-on Lab
 lab-proj1, python-dataframe (azure), python-lambda (azure)

  9/25Evaluation: loss function, confusion matrix, lift chart, ROC, AUCCIML 1.4, DM4 5evaluationhw2
 6 9/30Evaluation: accuracy, precision, recall, f1, type I, type II, numeric errors 
evaluationlab1lab1-sol
  10/2Credibility: confidence interval, cross-validation, bootstrapping, hyperparameterCIML 2.5-2.7, 5.5-5.7, DM4 5credibility
hw2-sol (10/6)
 7 10/7Proj 1 Discussion



  10/9Linear Models: linear regression, logistic regression, perceptron, winnowDM4 4.6, CIML 7, MMDS 12.2linear
proj1-sol (10/13)
 8 10/14Linear Models (continued)



  10/16 MIDTERM (Open Book)sampleCover all materials from 8/28 to 10/2
midterm-sol
 9 10/21Clustering: partitional vs. hierarchical, k-means vs. k-means++ vs. agglomerativeIDM2 7, CIML 3 & 15.1clustering

  10/23Project #2 Phase 1 Discussion

proj2
 10 10/28Support Vector Machine (SVM): support vectors, slack penalty, hinge loss, gradient decent, kernels
MMDS 12.3, CIML 7.7, 11.5-11.6
svm


  10/30Project #2 Phase 2 Discussion

hw3
 11 11/4Association rules mining, aprioriDM4 4, IDM2 5apriori

  11/6Feature Selection: filter, wrapper, embedded, forward stepwise, backward elimination, hill-climbing
DM4 8.1, CIML 5.1-5.4
featurelab2lab2-sol
 12 11/11Dimensionality Reduction: PCA, SVDMMDS 11, DM4 8, CIML 3.5dim-reduction

  11/13Ensemble Learning: bagging, randomization, boostingDM4 12, CIML 13, IDM2 4.10
ensemble
hw3-sol (11/17)
 13 11/18Neural NetworkIDM2 4.7-4.8, DM4 10neuralhw4
  11/20Ludwig LabLudwigludwig-lablab3lab3-sol (11/22)
 14 11/25
NO CLASS (Thanksgiving Holiday)



  11/27NO CLASS (Thanksgiving Holiday)


hw4-sol (12/1)
 15 12/2GAN, Deepfake, and ML Applications 
deepfakeml-apps

  12/4Semester Review
review
proj2 (12/8)
 16 12/9Presentation




  12/11Presentation




 1712/16 FINAL EXAM (Open Book) @ Westgate E208 (4:40-6:30pm) sampleCover all materials