Instructor: Dr. Isabel K. Darcy
Department of Mathematics and AMCS
University of Iowa
Office: MLH 25J
Phone: 319-335- 0770
Email: isabel-darcy AT uiowa.edu
Ways to get help:
Your grade will be based on
TENTATIVE CLASS SCHEDULE-ALL DATES SUBJECT TO CHANGE (click on date/section for file of corresponding class material):
| Tentative Schedule | HW/Announcements -- replace with references | |
| Week 1 | ||
|---|---|---|
| 1/20 | Introduction to data analysis | HW: Create github account |
| 1/22 | LAB 1: Introduction to Github 1, Introduction to Python, |
|
| Week 2 | ||
| 1/27 | Intro to ML , Linear Regression | HW: |
| 1/29 | LAB 2: Introduction to Github 2; Python: creating artificial data OR downloading real data, linear regression on unclean data | |
| Week 3 | ||
| 2/3 | Data cleaning,
Linear Regression, p-values |
HW: due Friday 2/6 Lab 1 |
| 2/5 | LAB 3: Introduction to Github 3, intro to cleaning data, linear regression on partially cleaned data | |
| Week 4 | ||
| 2/10 | Noise
Data cleaning, PCA, p-values, data cleaning Linear Regression, Variance |
HW: due Friday 2/13 Lab 2, Create personal webpage on Github |
| 2/12 | Lab 4: Introduction to Git, Intro to PCA, Linear regression | |
| Week 5 | ||
| 2/17 | Logistic Regression | HW: due Friday 2/20 Lab 3, Project draft 1, poster idea |
| 2/19 | Lab 5: | |
| Week 6 | ||
| 2/24 | Logistic Regression part 2 | HW: due Friday 2/27 Lab 4, poster draft |
| 2/26 | Lab 6: | |
| Week 7 | ||
| 3/3 | Presentations: Illustrate something you have learned.
Variance, PCA |
HW: due Friday 3/6 Lab 5, Project draft 2 |
| 3/5 | Review, Lab 7: | |
| Week 8 | ||
| 3/10 | Midterm Exam | HW: due Friday 3/13 Lab 6 |
| 3/12 | Lab 8: | |
| Week 9 | ||
| 3/23 | Test/Train, Data Leakage, Bias/Variance, Shrinkage | HW: due Friday 3/26 Lab 7, Idea for poster, python script |
| 3/25 | Shrinkage Lab 9: | |
| Week 10 | ||
| 3/31 | k-nearest neighbors, | HW: due Friday 4/3 Lab 8, Poster draft, Project draft 3 Voronoi (6:06 min) and k-means (9:10 min) |
| 4/2 | Lab 10: | |
| Week 11 | ||
| 4/7 | k-means clustering, Hierarchical Clustering, | HW: due Friday 4/10 Lab 9, |
| 4/9 | Clustering Lab 11: | |
| Week 12 | ||
| 4/14 | Presentations: Use artificial or real data to illustrate something. neural networks and deep learning |
HW: due Friday 4/17 Lab 10, Project draft 4 |
| 4/16 | neural networks and deep learning Lab 12: | |
| Week 13 | ||
| 4/21 | neural networks and deep learning | HW: due Friday 4/24 Lab 11, Poster draft 1 due Friday 4/19 |
| 4/23 | neural networks and deep learning Lab 13 | |
| Week 14 | ||
| 4/28 | Ethics, reproducible research, publication bias, data privacy | HW: due Friday 5/1 Lab 12, Project draft 5, Poster draft 2 due Friday 4/26 |
| 4/30 | Lab wrap-up | |
| Week 15 | ||
| 5/5 | Presentations | HW: due Friday 5/8 Lab 13, Poster draft 3 and written project |
| 5/7 | Presentations, Review | |
| Final's week | ||
| TBA | Final exam: TBA | |
References:
An Introduction to Statistical Learning with applications in Python, http://www.statlearning.com
Scikit-learn Machine Learning in Python, available at https://scikit-learn.org
StatQuest: eg: https://statquest.org/video-index/, https://statquest.org/neural-networks-part-1-inside-the-black-box/