STAT 451: Tentative Schedule
(Syllabus)
| Day #: Date | Subject | Homework Due (11:59 p.m.) |
| 1: Mo 6/16/25 video (71:35) |
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 Mo 6/23 to accommodate Fr 6/20 add class deadline) |
| 2: Tu 6/17 video (73:48) |
02 Juypter Notebook (4) (JupyterExample.ipynb/.html) 01 Introduction, continued: SVM |
Q01: calculator (extended to 6/23) (login help) |
| 3: We 6/18 video (61:04) |
(01separatingHyperplane.ipynb/.html) [02 Notation and Definitions: optional reading] 03 Sequences (2) (stringsDemo.ipynb, TuplesListsDemo.ipynb) |
Q02: Jupyter (extended to 6/23) |
| 4: Th 6/19 video (57:10) |
03 Fundamental Algorithms, Part 1: linear
regression (4) (03linearRegression.ipynb/.html) |
Q03: sequences (extended to 6/23) |
| 5: Mo 6/23 video (65:39) |
04_NumPy (2) (04_numpy.ipynb) |
|
| 6: Tu 6/24 video (70:39) |
03 Fundamental Algorithms, Part 2: logistic regression:
logistic regression (4) (03logisticRegression.ipynb/.html) |
HW01: SVM, linear regression |
| 7: We 6/25 video (37:18) |
05 pandas (2) (05_pandasDemo.ipynb) |
Q04: NumPy |
| 8: Th 6/26 video (70:18) |
03 Fundamental Algorithms, Part 3: decision tree (4)
(03decisionTree.ipynb/.html) |
Q05: pandas |
| 9: Mo 6/30 video (38:19) |
06 matplotlib (3) (06_matplotlibDemo.ipynb) continue decision tree |
|
| 10: Tu 7/1 video (44:51) |
03 Fundamental Algorithms, Part 4: more on SVM (4)
(03SVM.ipynb/.html) |
HW02: logistic regression, decision tree |
| 11: We 7/2 video (72:46) |
03 Fundamental Algorithms, Part 5: k-NN (2)
(03kNN.ipynb/.html) 07 write functions (2) |
Q06 matplotlib |
| [Th 7/3, Mo 7/7] |
[7/3: no class--day before Independence Day 7/7: no class--day off to match spring number of class days] |
|
| 12: Tu 7/8 video (67:07) |
04 Anatomy of a Learning Algorithm: 04.pdf
(4): gradient descent, scikit-learn
(04gradientDescent.ipynb/.html) |
Q07 functions |
| 13: We 7/9 video (35:23) |
05 Basic Practice, Part 1: feature
engineering (4)
(05featureEngineering.ipynb/.html) |
|
| 14: Th 7/10 video (64:12) |
Discuss exam rules conditional expressions (2) 05 Basic Practice, Part 2: algorithm / data split / model fit / regularize (4) (05modelFitRegularize.ipynb/.html) |
HW03: more SVM, kNN, gradient descent, feature engineering |
| 15: Mo 7/14 video (64:53) |
(optional) Q&A review |
Q08 conditional expressions |
| 16: Tu 7/15 |
Exam1 online in a 75-minute period of your choice today (rules, spring2022/key, fall2022/key, spring2023/key, summer2023/key, summer2024/key, summer 2025/key, histogram, midterm grades) |
Exam1 |
| 17: We 7/16 video (53:12) |
05 Basic Practice, Part 3: assessment / hyperparameter tuning / cross-validation (5) (05assessmentTuningCV.ipynb/.html) |
Project: form a group |
| 18: Th 7/17 video (71:18) |
05 Basic Practice, Part 3, continued: tuning & CV 07 Problems and Solutions, Part 1: kernel regression (2) (07kernelRegression.ipynb/.html) |
HW04: feature engineering, data split,
model fit and regularization |
| 19: Mo 7/21 video (55:01) |
07 Problems and Solutions, Part
2: multiclass,
one-class, and multilable classification (4) |
|
| 20: Tu 7/22 video (59:56) |
07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html) |
|
| 21: We 7/23 video (73:20) |
08 Advanced Practice: 08.pdf (5):
imbalance, combining/stacking, efficiency, multicore
(08imbalance_stacking_timing_multicore.ipynb/.html) |
Project: proposal |
| 22: Th 7/24 video (72:59) |
09 Unsupervised Learning: 09.pdf (4)
(09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html) |
HW05: algorithm selection, multiclass
classification, assesment,
tuning, ensemble learning,
imbalance |
| 23: Mo 7/28 |
Project proposal feedback: meet via Zoom with teacher
and/or TA |
Project proposal feedback meeting |
| 24: Tu 7/29 video (9:20) |
09 Unsupervised Learning, continued (just PCA benefits and perspective) Project help |
|
| 25: We 7/30 |
(optional) Q&A review |
|
| 26: Th 7/31 |
Exam2 online in a 75-minute period of your choice
today
(rules, summer2023/key, summer2024/key, summer2025/key) |
Exam2 |
| 27: Mo 8/4 | Project help | |
| 28: Tu 8/5 | Project help |
Project: turn in slides and video presentation |
| 29: We 8/6 |
Project: watch first 1/2 of presentations |
Project: peer feedback on first 1/2 |
| 30: Th 8/7 |
Project: watch second 1/2 of presentations |
Project: peer feedback on second 1/2 Project: report |
Note: