STAT 451: Tentative Schedule

(Syllabus)

Day #: Date	Subject	Homework Due (11:59 p.m.)
1: Th 9/4/25	Help with Anaconda installation 01 Introduction (6): course overview; SVM 01 Python as a Calculator (1)	Read introductory email Q00: background survey (extended to Tu 9/16 to accommodate Fr 9/12 add deadline)
2: Tu 9/9	02 Juypter Lab (4) (JupyterExample.ipynb/.html) 02 Juypter Lab (4) 01 Introduction, continued	Q01: calculator (extended to 9/16) (login help)
3: Th 9/11	SVM (01separatingHyperplane.ipynb/.html) [02 Notation and Definitions: optional reading] 03 Sequences (2) (stringsDemo.ipynb/.html, TuplesListsDemo.ipynb/.html)	Q02: Jupyter (extended to 9/16)
4: Tu 9/16	03 Fundamental Algorithms, Part 1: linear regression (4) (03linearRegression.ipynb/.html) Discuss HW01	(Q03 was here originally)
5: Th 9/18	03 linear regression, continued 04_NumPy (2) (04_numpy.ipynb/.html)	Q03: sequences
6: Tu 9/23	03 Fundamental Algorithms, Part 2: logistic regression: logistic regression (4) (03logisticRegression.ipynb/.html) 05 pandas (2) (05_pandas.ipynb/.html)	HW01: SVM, linear regression
7: Th 9/25	03 Fundamental Algorithms, Part 3: decision tree (4) (03decisionTree.ipynb/.html)	Q04: NumPy
8: Tu 9/30	logistic regression, continued: regularization decision tree, continued: code examples after 8x2 by hand; complete handout 06 matplotlib (3) (06_matplotlib.ipynb/.html)	Q05: pandas (extended to 10/10/25)
9: Th 10/2	matplotlib, continued (start with annotations) Discuss project.pdf (linked below in Day 17 line) 03 Fundamental Algorithms, Part 4: more on SVM (4) (03SVM.ipynb/.html)
10: Tu 10/7	more on SVM, continued 07 write functions (2)	HW02: logistic regression, decision tree
11: Th 10/9	07 write functions, continued 03 Fundamental Algorithms, Part 5: k-NN (2) (03kNN.ipynb/.html)	Q06 matplotlib
12: Tu 10/14	k-NN, continued 04 Anatomy of a Learning Algorithm (4): gradient descent, scikit-learn (04gradientDescent.ipynb/.html)	Q07 functions
13: Th 10/16	05 Basic Practice, Part 1: feature engineering (6) (05featureEngineering.ipynb/.html)
14: Tu 10/21	Discuss exam rules 05 Basic Practice, Part 2: algorithm / data split / model fit / regularize (4) (05modelFitRegularize.ipynb/.html) 08 conditional expressions (2)	HW03: more SVM, kNN, gradient descent, feature engineering
15: Th 10/23	05 Basic Practice, Part 2, continued (code from "Loop through"; Lasso & Ridge; code again) discuss project (optional) Q&A review	Q08 conditional expressions
16: Tu 10/28	Midterm exam in class (rules, spring2023/key, fall2023/key; spring 2024/key, fall2024/key, spring 2025/key, fall 2025 broken 6h/fall 2025/key, histogram, midterm grades)	Midterm exam
17: Th 10/30	05 Basic Practice, Part 3: assessment / hyperparameter tuning / cross-validation (5) (05assessmentTuningCV.ipynb/.html)	Project: form a group
18: Tu 11/4	Review project groups 05 Basic Practice, Part 3, continued: tuning & CV 07 Problems and Solutions, Part 1: kernel regression (1) (07kernelRegression.ipynb/.html)
19: Th 11/6	07 Problems and Solutions, Part 2: multiclass, one-class, and multilable classification (4) work on project proposals	HW04: feature engineering, data split, model fit and regularization
20: Tu 11/11	07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html)	Project: proposal
21: Th 11/13	schedule feedback meetings 08 Advanced Practice: 08.pdf (5): imbalance, combining/stacking, efficiency, multicore (08imbalance_stacking_timing_multicore.ipynb/.html)
22: Tu 11/18	project proposal feedback meet in class with teacher and/or TA	Project proposal feedback meeting; HW05: algorithm selection, multiclass classification, assesment, tuning, ensemble learning, imbalance
23: Th 11/20	08 Advanced Practice, continued (here or Tu 11/18): timing, multicore 09 Unsupervised Learning: 09.pdf (7) (09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html)
24: Tu 11/25	set presentation schedule 09 Unsupervised Learning, continued: clustering, PCA project help
[Th 11/27]	[no class--Thanksgiving]
25: Tu 12/2	09 Unsupervised Learning, continued: PCA (last 2 benefits; code example) project help	We 12/3: Project: turn in slides
26: Th 12/4	mention exam room & materials; review session Project: first 1/2 of presentations	Project: peer feedback on first 1/2
27: Tu 12/9	Project: second 1/2 of presentations	Project: peer feedback on second 1/2 Project: report; peer feedback (2-3 sentences) due We 12/10
[Th 12/11]	[3:00-4:00 optional Q&A review in our usual room]
Sa 12/13	Final exam: 7:25PM-9:25PM in Social Sciences 5206 (rules, fall2024/key, spring2025/key)	Final exam

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.
• Orange 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.