STAT 451: Tentative Schedule
(Syllabus)
Day #: Date  Subject  Homework Due (11:59 p.m.) 
1: Mo 6/17/24 video (71:35) 
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 Mo 6/24 to accommodate Fr 6/21 add class deadline) 
2: Tu 6/18 video (73:48) 
02 Juypter Notebook (4) (JupyterExample.ipynb/.html) 01 Introduction, continued: SVM 
Q01: calculator (extended to 6/24) (login help) 
3: We 6/19 video (61:04) 
(01separatingHyperplane.ipynb/.html) [02 Notation and Definitions: optional reading] 03 Sequences (2) (stringsDemo.ipynb, TuplesListsDemo.ipynb) 
Q02: Jupyter (extended to 6/24) 
4: Th 6/20 video (57:10) 
03 Fundamental Algorithms, Part 1: linear
regression (4) (03linearRegression.ipynb/.html) 
Q03: sequences (extended to 6/24) 
5: Mo 6/24 video (75:39) 
04_NumPy (2) (04_numpy.ipynb) 

6: Tu 6/25 video (70:39) 
03 Fundamental Algorithms, Part 2: logistic regression:
logistic regression (4) (03logisticRegression.ipynb/.html) 
HW01: SVM, linear regression 
7: We 6/26 video (37:18) 
05 pandas (2) (05_pandasDemo.ipynb) 
Q04: NumPy 
8: Th 6/27 video (70:18) 
03 Fundamental Algorithms, Part 3: decision tree (4)
(03decisionTree.ipynb/.html) 
Q05: pandas 
9: Mo 7/1 video (38:19) 
06 matplotlib (3) (06_matplotlibDemo.ipynb) continue decision tree 

10: Tu 7/2 video (44:51) 
03 Fundamental Algorithms, Part 4: more on SVM (4)
(03SVM.ipynb/.html) 
HW02: logistic regression, decision tree 
11: We 7/3 video (72:46) 
03 Fundamental Algorithms, Part 5: kNN (2)
(03kNN.ipynb/.html) 07 write functions (2) 
Q06 matplotlib 
[Th 7/4, Mo 7/8] 
[7/4: no classIndependence Day 7/8: no classday off to match spring number of class days] 

12: Tu 7/9 video (67:07) 
04 Anatomy of a Learning Algorithm: 04.pdf
(4): gradient descent, scikitlearn
(04gradientDescent.ipynb/.html) 
Q07 functions 
13: We 7/10 video (35:23) 
05 Basic Practice, Part 1: feature
engineering (4)
(05featureEngineering.ipynb/.html) 

14: Th 7/11 video (64:12) 
Discuss exam rules 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: Mo 7/15 video (64:53) 
(optional) Q&A review 
Q08 conditional expressions 
16: Tu 7/16 
Exam1 online in a 75minute period of your choice on 7/16 (rules, spring2022/key, fall2022/key, spring2023/key, summer2023/key, summer2024/key) 
Exam1 
17: We 7/17 video (53:12) 
05 Basic Practice, Part 3: assessment / hyperparameter tuning / crossvalidation (5) (05assessmentTuningCV.ipynb/.html) 
Project: form a group 
18: Th 7/18 video (71:18) 
05 Basic Practice, Part 3, continued: tuning & CV 07 Problems and Solutions, Part 1: kernel regression (2) (07kernelRegression.ipynb/.html) 
HW04: feature engineering, data split,
model fit and regularization 
19: Mo 7/22 video (55:01) 
07 Problems and Solutions, Part
2: multiclass,
oneclass, and multilable classification (4) 

20: Tu 7/23 video (59:56) 
07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html) 

21: We 7/24 video (73:20) 
08 Advanced Practice: 08.pdf (5):
imbalance, combining/stacking, efficiency, multicore
(08imbalance_stacking_timing_multicore.ipynb/.html) 
Project: proposal 
22: Th 7/25 video (72:59) 
09 Unsupervised Learning: 09.pdf (4)
(09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html) 
HW05: algorithm selection, multiclass
classification, assesment,
tuning, ensemble learning,
imbalance 
23: Mo 7/29 
Project proposal feedback: meet via Zoom with teacher
and/or TA 
Project proposal feedback meeting 
24: Tu 7/30 video (9:20) 
09 Unsupervised Learning, continued (just PCA benefits and perspective) Project help 

25: We 7/31 
(optional) Q&A review 

26: Th 8/1 
Exam2 online in a 75minute period of your choice on 8/1
(rules, summer2023/key) 
Exam2 
27: Mo 8/5  Project help  
28: Tu 8/6  Project help 
Project: turn in slides and video presentation 
29: We 8/7 
Project: watch first 1/2 of presentations 
Project: peer feedback on first 1/2 
30: Th 8/8 
Project: watch second 1/2 of presentations 
Project: peer feedback on second 1/2 Project: report 
Note: