STAT 451: Tentative Schedule

(Syllabus)

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

Note: