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 late-add 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: k-NN (2) (03kNN.ipynb/.html)	Q06 matplotlib
12: 3/2	07 write functions (2) 04 Anatomy of a Learning Algorithm: 04.pdf (4): gradient descent, scikit-learn (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 / cross-validation (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, one-class, 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 2:45-4:45 Tu 5/9	Final exam 2:45-4:45 Tu 5/9

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.