Announcements! ( See All )
01/17 - Spring semester instruction begins on 01/22. First lecture will be 01/22!
01/22 - Homework 0 is out. Please try out Gradescope with entry code 9J7BJB
01/29 - Homework 1 is out.
WeekDateContentPrep & Assignments
1 Tue 01/22 Lecture 1: Introduction and quick review of math concepts Week 1 Reading and Guide
HW 0 (Try out Gradescope, due 01/30 6pm)
Thu 01/29 Lecture 2: Problem formulation with a case study and data collection HW 1 (Matrix algebra, Due 02/06)
2 Mon 01/28 Lab
Tue 01/29 Lecture 3: Introduction to PCS, internal and external validity Week 2 Guide
Thu 01/31 Lecture 4: Cross-validation (CV), PQR-S
3 Mon 02/04 Lab
Tue 02/05 Lecture 5: Data preprocessing Week 3 Guide
Thu 02/07 Lecture 6: Exploratory data analysis (EDA) HW 2 (PCA, Due 02/20)
4 Mon 02/11 Lab
Tue 02/12 Lecture 7: EDA continued: PCA, K-means Week 4 Guide
Thu 02/14 Lecture 8: EDA continued: other unsupervised learning methods
5 Mon 02/18 Lab
Tue 02/19 Lecture 9: K-means, GMM and EM Week 5 Guide
Thu 02/21 Lecture 10: Linear regression Project 1 (EDA on Redwoord data, Due 03/08) + Short HW3
6 Mon 02/25 Lab
Tue 02/26 Lecture 11: Regularizations in linear regression Week 6 Guide
Thu 02/28 Lecture 12: Regularizations in linear regression
7 Mon 03/04 Lab
Tue 03/05 Lecture 13: Model selection Week 7 Guide
Thu 03/07 Lecture 14: Model Assessment HW 4 (Linear regression, TBA, Due 03/20)
8 Mon 03/11 Lab
Tue 03/12 Lecture 15: Bias-variance trade-off Week 8 Guide
Thu 03/14 Lecture 16: Classification: introduction
9 Mon 03/18 Lab
Tue 03/19 Lecture 17: Classification: introduction Week 9 Guide
Thu 03/21 Midterm in class HW 5 (Classification, TBA, Due 04/03)
10 Tue 03/26 Spring break
Thu 03/28 Spring break
11 Mon 04/01 Lab
Tue 04/02 Lecture 18: Classification: logistic regression Week 11 Guide
Thu 04/04 Lecture 19: Classification: Support vector machine (SVM) Project 2 (Classification on cloud data, Due 04/17)
12 Mon 04/08 Lab
Tue 04/09 Lecture 20: Kernel methods Week 12 Guide
Thu 04/11 Lecture 21: Kernel methods
13 Mon 04/15 Lab
Tue 04/16 Lecture 20: Nearest Neighbor Week 13 Guide
Thu 04/18 Lecture 21: Tree-based methods HW 6 (Nonlinear methods, Due 04/31)
14 Mon 04/22 Lab
Tue 04/23 Lecture 22: Boosting algorithms Week 14 Guide
Thu 04/25 Lecture 23: Neural networks
15 Mon 04/29 Lab
Tue 04/30 Lecture 24: Interpretable machine learning Week 15 Guide
Thu 05/02 Lecture 25: Final Review
16 Tue 05/07 RRR Week
Thu 05/09 RRR Week
17 Mon 05/13 FINAL EXAM TBA