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:

• Black lines in this schedule correspond to materials on machine learning. A black number at the start of a phrase, like the "01" in "01 Introduction", indicates a chapter in the optional reference book, The Hundred-Page Machine Learning Book by Andriy Burkov.
• Green lines correspond to materials on Python basics, which we study to allow using the scikit-learn library to apply machine learning techniques to real data.
• A number in parentheses, like "(2)", indicates the number of pages in a handout to help me with planning. I am developing materials as the course proceeds and will revise the schedule as needed.