STAT 451: Tentative Schedule

(Syllabus)

Day #: Date Subject Homework Due (11:59 p.m.)
1: Th 9/7/23
 Help with Anaconda installation
01 Introduction (6): course overview; SVM
01 Python as a Calculator (1)
 Read introductory email
Q00: background survey (extended to Mo 9/18 to accommodate Fr 9/15 add class deadline)
2: Tu 9/12
 (Q&A reply: STAT/DS Quick Talks from spring 2023)
02 Juypter Notebook (4) (JupyterExample.ipynb/.html)
01 Introduction, continued: SVM
 Q01: calculator (extended to 9/18)
(login help)
3: Th 9/14
 (01separatingHyperplane.ipynb/.html)
[02 Notation and Definitions: optional reading]
03 Sequences (2) (stringsDemo.ipynb, TuplesListsDemo.ipynb)
 Q02: Jupyter (extended to 9/18)
4: Tu 9/19
 03 Fundamental Algorithms, Part 1: linear regression (4) (03linearRegression.ipynb/.html)
5: Th 9/21
 04_NumPy (2) (04_numpy.ipynb/.html)
Q03: sequences
6: Tu 9/26
 03 Fundamental Algorithms, Part 2: logistic regression: logistic regression (4) (03logisticRegression.ipynb/.html)
 HW01: SVM, linear regression
7: Th 9/28
 05 pandas (2) (05_pandas.ipynb/.html)
practice: select subsets of NFL data;
make SVM, linear regression, and logistic regression models
 Q04: NumPy
8: Tu 10/3
 03 Fundamental Algorithms, Part 3: decision tree (4) (03decisionTree.ipynb/.html)
 Q05: pandas
9: Th 10/5
 06 matplotlib (3) (06_matplotlib.ipynb/.html)
continue decision tree
10: Tu 10/10
 03 Fundamental Algorithms, Part 4: more on SVM (4) (03SVM.ipynb/.html)
HW02: logistic regression, decision tree
11: Th 10/12
 03 Fundamental Algorithms, Part 5: k-NN (2) (03kNN.ipynb/.html)
07 write functions (2)
 Q06 matplotlib
12: Tu 10/17
 04 Anatomy of a Learning Algorithm: 04.pdf (4): gradient descent, scikit-learn (04gradientDescent.ipynb/.html)
13: Th 10/19
 05 Basic Practice, Part 1: feature engineering (6) (05featureEngineering.ipynb/.html)
 Q07 functions
14: Tu 10/24
 Discuss exam rules
08 conditional expressions (2)
05 Basic Practice, Part 2: algorithm / data split / model fit / regularize (4) (05modelFitRegularize.ipynb/.html)
 HW03: more SVM, kNN, gradient descent, feature engineering
15: Th 10/26
 (optional) Q&A review
 Q08 conditional expressions
16: Tu 10/31 Midterm exam in class (rules, spring2022/key, fall2022/key, spring2023/key, fall2023/key; histogram, midterm grades)
 Midterm exam
17: Th 11/2
 05 Basic Practice, Part 3: assessment / hyperparameter tuning / cross-validation (5) (05assessmentTuningCV.ipynb/.html)
18: Tu 11/7
 05 Basic Practice, Part 3, continued: tuning & CV
07 Problems and Solutions, Part 1: kernel regression (2) (07kernelRegression.ipynb/.html)
 Project: form a group
19: Th 11/9
 07 Problems and Solutions, Part 2: multiclass, one-class, and multilable classification (4)
 HW04: feature engineering, data split, model fit and regularization
20: Tu 11/14
 07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html)
21:Th 11/16
 08 Advanced Practice: 08.pdf (5): imbalance, combining/stacking, efficiency, multicore (08imbalance_stacking_timing_multicore.ipynb/.html)
 Project: proposal (extended to Su 11/19)
22: Tu 11/21 project proposal feedback: meet in class with teacher and/or TA
9:30 schedule, 11:00 schedule 		Project proposal feedback meeting
HW05: algorithm selection, multiclass classification, assesment, tuning, ensemble learning, imbalance
[Th 11/23] [no class--Thanksgiving]
23: Tu 11/28
 09 Unsupervised Learning: 09.pdf (7) (09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html)
24: Th 11/30
 confirm presentations schedule: 9:30, 11:00
09 Unsupervised Learning, continued: k-means code, PCA
project help
 Mo 12/4 Project: turn in slides
25: Tu 12/5 Project: first 1/2 of presentations
 Project: peer feedback on first 1/2
26: Th 12/7 Project: second 1/2 of presentations
 Project: peer feedback on second 1/2
27: Tu 12/12
 (optional) Q&A review
 Project: report
Sa 12/16 Final exam: Sa 12/16 7:25PM-9:25PM, Chamberlin 2103 (rules, sample exam/key )
 Final exam

Note: