STAT 451: Tentative Schedule

(Syllabus)

GroupD quiz
Day #: Date Subject Homework Due (11:59 p.m.)
1: Tu 1/20/26
 Help with Anaconda installation
01 Introduction (6): course overview; SVM
01 Python as a Calculator (1)
B2590 tables
 Read introductory email
Q00: background survey (extended to Tu 2/3 to accommodate Fr 1/30 add deadline)
2: Th 1/22
 01 Introduction, continued
SVM (01separatingHyperplane.ipynb/.html)
GroupA practice/key
 Q01: calculator (extended to 2/3)
(login help)
3: Tu 1/27
 a few minutes of Q&A
GroupA quiz (in class)
02 Juypter Lab (4) (JupyterExample.ipynb/.html)
[02 Notation and Definitions: optional reading]
03 Sequences (2) (stringsDemo.ipynb/.html, TuplesListsDemo.ipynb/.html)
 GroupA quiz (in class)
4: Th 1/29
 Discuss HW01
03 Fundamental Algorithms, Part 1: linear regression (4) (03linearRegression.ipynb/.html)
 Q02: Jupyter (extended to 2/3)
5: Tu 2/3
 03 linear regression, continued
04_NumPy (2) (04_numpy.ipynb/.html)
GroupB practice/key
 Q03: sequences (extended to 2/3)
6: Th 2/5
 Q&A; then GroupB quiz
03 Fundamental Algorithms, Part 2: logistic regression: logistic regression (4) (03logisticRegression.ipynb/.html)
 GroupB quiz
HW01: SVM, linear regression
7: Tu 2/10
 03 logistic regression, continued (start with MLE)
05 pandas (2) (05_pandas.ipynb/.html)
 Q04: NumPy
8: Th 2/12
 06 matplotlib (3) (06_matplotlib.ipynb/.html)
GroupC practice/key; bring calculator for GroupC quiz
03 Fundamental Algorithms, Part 3: decision tree (4) (03decisionTree.ipynb/.html)
 Q05: pandas
9: Tu 2/17
 Q&A; then GroupC quiz (bring calculator)
decision tree, continued: p. 2+
 GroupC quiz
10: Th 2/19
 Discuss project.pdf (linked below in Day 17 line)
03 Fundamental Algorithms, Part 4: more on SVM (4) (03SVM.ipynb/.html)
HW02: logistic regression, decision tree
11: Tu 2/24
 FYI, new Canvas discussion Looking for Group Members?
03 More SVM, continued (p. 4 notes, RBF code example)
07 write functions (2)
03 Fundamental Algorithms, Part 5: k-NN (2) (03kNN.ipynb/.html)
GroupD practice/key
 Q06 matplotlib
12: Th 2/26
 GroupD quiz
k-NN, continued
04 Anatomy of a Learning Algorithm (4): gradient descent, scikit-learn (04gradientDescent.ipynb/.html)
 GroupD quiz
Q07 functions
13: Tu 3/3
 k-NN regression code example
gradient descent, continued
05 Basic Practice, Part 1: feature engineering (6) (05featureEngineering.ipynb/.html)
14: Th 3/5
 05 Basic Practice, Part 2: algorithm / data split / model fit / regularize (4) (05modelFitRegularize.ipynb/.html)
 HW03: more SVM, kNN, gradient descent, feature engineering
15: Tu 3/10
 reminder (optional): form project groups by Tu 3/17/26
05 Basic Practice, Part 2, continued (from "Underfitting ...")
GroupE practice/key
(optional) Q&A review
16: Th 3/12 Midterm exam in class (rules, spring2023/key, fall2023/key; spring 2024/key, fall2024/key, spring 2025/key, fall 2025/key, spring 2026 histogram, midterm grades)
Midterm exam
17: Tu 3/17
 08 conditional expressions (2)
05 Basic Practice, Part 3: assessment / hyperparameter tuning / cross-validation (5) (05assessmentTuningCV.ipynb/.html)
 Project: form a group
18: Th 3/19
 05 Basic Practice, Part 3, continued (from "(c, c) is on ROC curve"
GroupF practice/key
 Q08 conditional expressions
19: Tu 3/24
 Please sit in "Project" groups
GroupF quiz
07 Problems and Solutions, Part 1: kernel regression (1) (07kernelRegression.ipynb/.html)
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: Th 3/26
 multiclass, etc, continued
07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html)
optional (there will be no GroupG quiz): GroupG practice/key
 Project: proposal
[Tu 3/31, Th 4/2] [no class--spring break]
21: Tu 4/7
 schedule feedback meetings
08 Advanced Practice: 08.pdf (5): imbalance, combining/stacking, efficiency, multicore (08imbalance_stacking_timing_multicore.ipynb/.html)
22: Th 4/9 project proposal feedback: in class with teacher and/or TA
 Project proposal feedback meeting;
HW05: algorithm selection, multiclass classification, assesment, tuning, ensemble learning, imbalance
23: Tu 4/14
 FYI: undergraduate research
FYI: sports analytics talks
08 Advanced Practice, continued: timing, multicore
GroupH practice/key coming soon (please sit in "Project" groups)
09 Unsupervised Learning: 09.pdf (7) (09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html)
24: Th 4/16
 GroupH quiz
set presentation schedule
09 Unsupervised Learning, continued: DBSCAN clustering, PCA
project help
 GroupH quiz
25: Tu 4/21 FinalExam rules, etc., posted below
09 Unsupervised Learning, continued: PCA code & examples
project help
 We 4/22: Project: turn in slides
26: Th 4/23 Project: first 1/2 of presentations
 Project: peer feedback on first 1/2
27: Tu 4/28
 Project: second 1/2 of presentations
 Project: peer feedback on second 1/2
Project: report due Tu 4/28;
report peer feedback due We 4/29 (2-3 sentences; no AI)
28: Th 4/30
 (optional) Q&A review
Su 5/3
 Final exam: 7:45AM-9:45AM, Humanities 2340 (rules, fall2024/key, spring2025/key, spring 2026 histogram, grades)
 Final exam

Note: