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 lateadd 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: kNN (2)
(03kNN.ipynb/.html) 
Q06 matplotlib 
12: 3/2 
07 write functions (2) 04 Anatomy of a Learning Algorithm: 04.pdf (4): gradient descent, scikitlearn (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 / crossvalidation (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,
oneclass, 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 2:454:45 Tu 5/9 
Final exam 2:454:45 Tu 5/9 
Note: