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 |
|
| | 8/28 | Introduction to ML | DM4 1, CIML 1.2, MMDS 1 | intro, lab-personality | | hw0 | | 2 | 9/2
| NO CLASS (Labor Day) |
|
|
|
| | | 9/4 | Inputs and Outputs: nominal, ordinal, interval, ratio | DM4 2-3, IDM2 2.1
| in-out, lab-iris |
|
| | 3 | 9/9 | Jupyter Notebook Lab | Weka, Jupyter | lab-jupyter, lab-heart-disease (azure) |
|
| | | 9/11 | Distance: euclidean, minkowski, mahalanobis, mutual information | IDM2 2.4 | similarity | hw1 |
| | 4 | 9/16 | Decision tree: hunt's algorithm, gini index, entropy, information gain
| CIML h 1, IDM2 3.3, DM4 4.3
| decision-tree |
|
| | | 9/18 | 1R and Naive Bayes Classification | IDM2 4.4, DM4 4.2, CIML 9.3 | naive-bayes | proj1
| hw1-sol (9/22) | | 5 | 9/23 | Project #1 Hands-on Lab |
| lab-proj1, python-dataframe (azure), python-lambda (azure) |
|
| | | 9/25 | Evaluation: loss function, confusion matrix, lift chart, ROC, AUC | CIML 1.4, DM4 5 | evaluation | hw2 |
| | 6 | 9/30 | Evaluation: accuracy, precision, recall, f1, type I, type II, numeric errors |
| evaluation | lab1 | lab1-sol | | | 10/2 | Credibility: confidence interval, cross-validation, bootstrapping, hyperparameter | CIML 2.5-2.7, 5.5-5.7, DM4 5 | credibility |
| hw2-sol (10/6) | | 7 | 10/7 | Proj 1 Discussion |
|
|
|
| | | 10/9 | Linear Models: linear regression, logistic regression, perceptron, winnow | DM4 4.6, CIML 7, MMDS 12.2 | linear |
| proj1-sol (10/13) | | 8 | 10/14 | Linear Models (continued) |
|
|
|
| | | 10/16 | MIDTERM (Open Book) | sample | Cover all materials from 8/28 to 10/2 |
| midterm-sol | | 9 | 10/21 | Clustering: partitional vs. hierarchical, k-means vs. k-means++ vs. agglomerative | IDM2 7, CIML 3 & 15.1 | clustering |
|
| | | 10/23 | Project #2 Phase 1 Discussion |
|
| proj2 |
| | 10 | 10/28 | Support Vector Machine (SVM): support vectors, slack penalty, hinge loss, gradient decent, kernels
| MMDS 12.3, CIML 7.7, 11.5-11.6 |
svm |
|
| | | 10/30 | Project #2 Phase 2 Discussion |
|
| hw3 |
| | 11 | 11/4 | Association rules mining, apriori | DM4 4, IDM2 5 | apriori |
|
| | | 11/6 | Feature Selection: filter, wrapper, embedded, forward stepwise, backward elimination, hill-climbing
| DM4 8.1, CIML 5.1-5.4
| feature | lab2 | lab2-sol | | 12 | 11/11 | Dimensionality Reduction: PCA, SVD | MMDS 11, DM4 8, CIML 3.5 | dim-reduction |
|
| | | 11/13 | Ensemble Learning: bagging, randomization, boosting | DM4 12, CIML 13, IDM2 4.10
| ensemble |
| hw3-sol (11/17) | | 13 | 11/18 | Neural Network | IDM2 4.7-4.8, DM4 10 | neural | hw4 |
| | | 11/20 | Ludwig Lab | Ludwig | ludwig-lab | lab3 | lab3-sol (11/22) | | 14 | 11/25
| NO CLASS (Thanksgiving Holiday) |
|
|
|
| | | 11/27 | NO CLASS (Thanksgiving Holiday) |
|
|
| hw4-sol (12/1) | | 15 | 12/2 | GAN, Deepfake, and ML Applications |
| deepfake, ml-apps |
|
| | | 12/4 | Semester Review |
| review |
| proj2 (12/8)
| | 16 | 12/9 | Presentation
|
|
|
|
| | | 12/11 | Presentation
|
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| | 17 | 12/16 | FINAL EXAM (Open Book) @ Westgate E208 (4:40-6:30pm) | sample | Cover all materials | | |
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