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: kNN (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, scikitlearn
(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 / crossvalidation (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,
oneclass, 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 classThanksgiving] 

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: kmeans 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 7:25PM9:25PM, Chamberlin 2103 (rules, sample exam/key
) 
Final exam 
Note: