STAT 451: Tentative Schedule

DO NOT RELY ON THIS ROUGH DRAFT.
An official version will be posted by 9/4/25.

(Syllabus)

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

Note: