STAT 451: Tentative Schedule
(Syllabus)
| Day #: Date | Subject | Homework Due (11:59 p.m.) |
| 1: Tu 1/21/25 |
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 2/4 to accommodate Fr 1/31 add class deadline) |
| 2: Th 1/23 |
02 Juypter Lab (4) (JupyterExample.ipynb/.html) 02 Juypter Lab (4) 01 Introduction, continued: SVM |
Q01: calculator (extended to 2/4) (login help) |
| 3: Tu 1/28 |
(01separatingHyperplane.ipynb/.html) (continue with 1D graph) [02 Notation and Definitions: optional reading] 03 Sequences (2) (stringsDemo.ipynb/.html, TuplesListsDemo.ipynb/.html) |
Q02: Jupyter (extended to 2/4) |
| 4: Th 1/30 |
03 Fundamental Algorithms, Part 1: linear
regression (4) (03linearRegression.ipynb/.html) Discuss HW01 |
Q03: sequences (extended to 2/4) |
| 5: Tu 2/4 |
04_NumPy (2) (04_numpy.ipynb/.html) |
|
| 6: Th 2/6 |
03 Fundamental Algorithms, Part 2: logistic regression:
logistic regression (4) (03logisticRegression.ipynb/.html) |
HW01: SVM, linear regression |
| 7: Tu 2/11 |
05 pandas (2) (05_pandas.ipynb/.html) |
Q04: NumPy |
| 8: Th 2/13 |
03 Fundamental Algorithms, Part 3: decision tree (4)
(03decisionTree.ipynb/.html) |
Q05: pandas |
| 9: Tu 2/18 |
06 matplotlib (3) (06_matplotlib.ipynb/.html) continue decision tree |
|
| 10: Th 2/20 |
03 Fundamental Algorithms, Part 4: more on SVM (4)
(03SVM.ipynb/.html) |
HW02: logistic regression, decision tree |
| 11: Tu 2/25 |
03 Fundamental Algorithms, Part 5: k-NN (2)
(03kNN.ipynb/.html) 07 write functions (2) |
Q06 matplotlib |
| 12: Th 2/27 |
04 Anatomy of a Learning Algorithm (4): gradient descent, scikit-learn
(04gradientDescent.ipynb/.html) |
Q07 functions |
| 13: Tu 3/4 |
05 Basic Practice, Part 1: feature
engineering (6)
(05featureEngineering.ipynb/.html) |
|
| 14: Th 3/6 |
Discuss exam rules 08 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: Tu 3/11 |
discuss project (optional) Q&A review |
Q08 conditional expressions |
| 16: Th 3/13 |
Midterm exam in class |
Midterm exam |
| 17: Tu 3/18 |
05 Basic Practice, Part 3: assessment / hyperparameter tuning / cross-validation (5) (05assessmentTuningCV.ipynb/.html) |
Project: form a group |
| 18: Th 3/20 |
05 Basic Practice, Part 3, continued: tuning & CV 07 Problems and Solutions, Part 1: kernel regression (1) (07kernelRegression.ipynb/.html) |
|
| [Tu 3/25, Th 3/27] |
[no class: spring break] |
|
| 19: Tu 4/1 |
07 Problems and Solutions, Part
2: multiclass,
one-class, and multilable classification (4) |
HW04: feature engineering, data split,
model fit and regularization |
| 20: Th 4/3 |
07 Problems and Solutions, Part 3: ensemble learning (3) (07ensemble.ipynb/.html) regular expressions |
Project: proposal |
| 21: Tu 4/8 |
schedule feedback meetings 08 Advanced Practice: 08.pdf (5): imbalance, combining/stacking, efficiency, multicore (08imbalance_stacking_timing_multicore.ipynb/.html) |
|
| 22: Th 4/10 |
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: Tu 4/15 |
09 Unsupervised Learning: 09.pdf (7)
(09densityEstimation.ipynb/.html, 09clustering.ipynb/.html, 09PCA.ipynb/.html) |
|
| 24: Th 4/17 |
set presentation schedule 09 Unsupervised Learning, continued: PCA project help |
Mo 4/21: Project: turn in slides |
| 25: Tu 4/22 |
project help |
|
| 26: Th 4/24 |
Project: first 1/2 of presentations |
Project: peer feedback on first 1/2 |
| 27: Tu 4/29 |
Project: second 1/2 of presentations |
Project: peer feedback on second 1/2 |
| 28: Th 5/1 |
(optional) Q&A review |
Project: report; peer feedback (2-3 sentences) due Fr 5/2 |
| Su 5/4 | Final exam: 7:45-9:45 a.m. |
Final exam |
Note: