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 late-add 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: k-NN
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: