STAT 451: Tentative Schedule

(Syllabus)

Day #: Date Subject Homework Due (11:59 p.m.)
1: 1/24/23 Help with Anaconda installation
01 Introduction (6): course overview; SVM
01 Python as a Calculator (1) (Note: Python links are green)
Read introductory email
Q00: background survey
(extended to Sa 2/4 to accommodate late-add students)
2: 1/26 I corrected typo in schedule (below) and syllabus: midterm is "Th 3/23", not "Tu 3/23"
Please remind to record; and to stay by the podium
02 Juypter Notebook (4) (JupyterExample.ipynb/.html)
Discuss survey
01 Introduction, continued: SVM
Q01: calculator (extended to 2/4)
(login help)
3: 1/31 (01separatingHyperplane.ipynb/.html)
[02 Notation and Definitions: optional reading]
03 Sequences (2) (stringsDemo.ipynb, TuplesListsDemo.ipynb)
Q02: Jupyter (extended to 2/4)
4: 2/2 03 Fundamental Algorithms, Part 1: linear regression
Q03: sequences (extended to 2/4)
5: 2/7 04_NumPy (2) (04_numpy1demo.ipynb)
6: 2/9 03 Fundamental Algorithms, Part 2: logistic regression: logistic regression (4) (03logisticRegression.ipynb/.html)
HW01 2/10: SVM, linear regression
7: 2/14 05 pandas (2) (05_pandasDemo.ipynb)
Q04: NumPy
8: 2/16 03 Fundamental Algorithms, Part 3: decision tree (4) (03decisionTree.ipynb/.html)
Q05: pandas
9: 2/21 06 matplotlib (3) (06_matplotlibDemo.ipynb)
continue decision tree
10: 2/23 03 Fundamental Algorithms, Part 4: more on SVM (4) (03SVM.ipynb/.html)
HW02 2/24: logistic regression, decision tree
11: 2/28 03 Fundamental Algorithms, Part 5: k-NN (2) (03kNN.ipynb/.html)
Q06 matplotlib
12: 3/2 07 write functions (2)
04 Anatomy of a Learning Algorithm: 04.pdf (4): gradient descent, scikit-learn (04gradientDescent.ipynb/.html)
13: 3/7 05 Basic Practice, Part 1: feature engineering (4) (05featureEngineering.ipynb/.html)
Q07 functions
14: 3/9 Discuss exam rules
conditional expressions (2)
05 Basic Practice, Part 2: algorithm / data split / model fit / regularize (4) (05modelFitRegularize.ipynb/.html)
HW03 3/10: more SVM, kNN, gradient descent, SGD, feature engineering
[3/14,16] [spring break]
15: 3/21 introduce project
(optional) Q&A review
Q08
16: 3/23 Midterm exam Midterm exam Th 3/23 in class
17: 3/28 05 Basic Practice, Part 2, continued
Project Tu 3/28: form a group
18: 3/30 05 Basic Practice, Part 3: assessment / hyperparameter tuning / cross-validation (5) (05assessmentTuningCV.ipynb/.html)
HW04 3/31: feature engineering, data split, model fit and regularization
19: 4/4 07 Problems and Solutions, Part 1: kernel regression (2) (07kernelRegression.ipynb/.html)
20: 4/6 07 Problems and Solutions, Part 2: multiclass, one-class, and multilable classification (4)
21: 4/11 07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html)
Project Tu 4/11: proposal
22: 4/13 project proposal feedback: meet in class with teacher and/or TA
HW05 4/14: algorithm selection, multiclass classification, assesment, tuning, ensemble learning, imbalance
23: 4/18 08 Advanced Practice: 08.pdf (5): imbalance, combining/stacking, efficiency, multicore (08stackingTiming.ipynb/.html)
24: 4/20 Assign presentation order
09: Unsupervised Learning: 09.pdf (4) (09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html)
25: 4/25 project help
Project We 4/26: slides
26: 4/27 Project: first 1/2 of presentations Project Th 4/27: presentations and peer feedback
27: 5/2 Project: second 1/2 of presentations
Project Th 5/2: presentations and peer feedback
Project We 5/3: report
Project Fr 5/5: report peer feedback
28: 5/4 (optional) Q&A review
Exam week Final exam 2:45-4:45 Tu 5/9
Final exam 2:45-4:45 Tu 5/9

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