STAT 451: Tentative Schedule
(Syllabus)
Day #: Date  Subject  Homework Due (11:59 p.m.) 
1: 9/8/22 
Help with Anaconda installation demonstrate piazza 01 Introduction (6): course overview 01 Python as a Calculator (1) (Note: Python links are green) 
Read introductory email Q00: background survey 
2: 9/13 
02 Juypter Notebook (4) (JupyterExample.ipynb/.html) Discuss survey 01 Introduction, continued: SVM 
Q01: calculator (login help) (extended to Fr 9/16 to accommodate lateadd students) 
3: 9/15  (01separatingHyperplane.ipynb/.html) [02 Notation and Definitions: optional reading] 03 Sequences (2) (stringsDemo.ipynb, TuplesListsDemo.ipynb) 

4: 9/20 
03 Fundamental Algorithms, Part 1: linear
regression (4) (03linearRegression.ipynb/.html) 
Q02: Jupyter 
5: 9/22 
Discuss HW01 04_NumPy (2) (04_numpy1demo.ipynb) 
Q03: sequences 
6: 9/27 
03 Fundamental Algorithms, Part 2: logistic regressoin 
Q04: NumPy 
7: 9/29 
05 pandas (2) (05_pandasDemo.ipynb) 
HW01 9/30: SVM, linear regression 
8: 10/4 
finish pandas 03 Fundamental Algorithms, Part 3: decision tree 

9: 10/6 
06 matplotlib (06_matplotlibDemo.ipynb) finish decision tree 
Q05: pandas 
10: 10/11 
03 Fundamental Algorithms, Part 4: more on SVM 
Q06 matplotlib 
11: 10/13 
03 Fundamental Algorithms, Part 5: kNN 
HW02 10/14: logistic regression, decision tree 
12: 10/18 
07 write functions (2) 04 Anatomy of a Learning Algorithm 

13: 10/20 
04 Anatomy of a Learning Algorithm, continued 05 Basic Practice, Part 1 
Q07 functions 
14: 10/25 
05 Basic Practice, Part 2 

15: 10/27 
Discuss exam rules conditional expressions (2) 05 Basic Practice, Part 3 
HW03 10/28: more SVM, kNN, gradient descent 
16: 11/1 
Q&A review 
Q08 conditional expressions 
17: 11/3 
Midterm exam 
Midterm exam 11/3 in class Project 11/4: form a group 
18: 11/8 
05 Basic Practice, Part 3, continued 06 Neural Networks and Deep Learning 

19: 11/10 
07 Problems and Solutions, Part 1 
HW04 11/11: feature engineering, data split, fit, algorithm
selection, regularize, assesment, tuning 
20: 11/15 
07 Problems and Solutions, Part
2 

21: 11/17  07 Problems and Solutions, Part 3 
Project 11/18: proposal 
22: 11/22 
08 Advanced Practice 

[11/24] 
[Thanksgiving recess] 

23: 11/29 
project proposal feedback: meet in class with teacher and/or TA 

24: 12/1 
09: Unsupervised Learning 
HW05 12/2: neural nets, kernel regression, classification
variants, ensemble learning, imbalance, stacking, clustering 
25: 12/6 
Assign presentation order project help 
Project We 12/7: slides 
26: 12/8  Project: first 1/2 of presentations  
27: 12/13 
Project: second 1/2 of presentations 
Project Tu 12/13: presentation feedback Project We 12/14: report Project Tu 12/20 (2:25 pm): report feedback 
Note: