Instructors
Instructor: Ryan Giordano
Office: 389 Evans Hall
Office hours: 12-2pm Mondays
rgiordano@berkeley.edu
pronouns: He / him
GSI: Dohyeong Ki
Office: TBD Evans Hall
Office hours: 3:30pm-5:30pm Thursdays (subject to change)
dohyeong_ki@berkeley.edu
pronouns: He / him
Day-to-day announcements can be found in BCourses. Discussions can be found in ED. (See links above.)
Schedule
Lectures will be held Jan 16 2024 – May 03 2024 on Tuesday and Thursday, 9:30 am – 10:59 am, in Etcheverry 3108.
Labs will be held on Wednesdays from 9:00 am – 11:00 am and 1:00pm – 3:00 pm in Evans 344.
(Link to official course calendar.)
The following subject schedule is tentative and subject to change.
Week 1
| Jan 16: |
Lecture 1
Class organization. |
|
| Jan 18: |
Lecture 2
Simple regression: a quick review. |
|
Week 2
| Jan 23: |
Lecture 3
Multilinear regression as loss minimization |
|
| Jan 25: |
Lecture 4
Multilinear regression as projection |
|
Week 3
| Jan 30: |
Lecture 5
Different ways to draw lines through points |
|
| Feb 1: |
Lecture 6
Transformations of regressors: Some payoffs from the linear algebra perspective |
|
Week 4
| Feb 6: |
Lecture 7
Univariate statistics and limit theorems |
|
| Feb 8: |
Lecture 8
Vector and matrix-valued statistics and limit theorems |
|
Week 5
| Feb 13: |
Lecture 9
The Gaussian assumption |
|
| Feb 15: |
Lecture 10
Estimating prediction uncertainty: Estimating the residual variance |
|
Week 6
| Feb 20: |
Lecture 11
Consistency of OLS and the residual variance under the Gaussian assumptions |
|
| Feb 22: |
Lecture 12
Residual distribution under normality |
|
Week 7
| Feb 27: |
Lecture 13
Sampling variability of the coefficients |
|
| Feb 29: |
Lecture 14
Implications of Gaussianity (and deviations from it) |
|
Week 8
| Mar 5: |
Lecture 15
Machine learning assumptions and the sandwich covariance matrix |
|
| Mar 7: |
Lecture 16
Sampling variability of the coefficients |
|
Week 9
| Mar 12: |
Lecture 17
Interpreting the coefficients and R output |
|
| Mar 14: |
Lecture 18
Interpreting the coefficients: The FWL theorem |
|
Week 10
| Mar 19: |
Lecture 19
Confidence intervals and hypothesis testing |
|
| Mar 21: |
Lecture 20
Variable selection and the F-test |
|
Week 11
| Mar 25: |
Holiday
Spring break |
|
Week 12
| Apr 2: |
Lecture 21
Bias-variance tradeoff in prediction |
|
| Apr 4: |
Lecture 22
Ridge or L2 regression |
|
Week 13
| Apr 9: |
Lecture 23
LASSO or L1 regression |
|
| Apr 11: |
Lecture 24
Influence and Outliers |
|
Week 14
| Apr 16: |
Lecture 25
Omitted variables in inference and prediction |
|
| Apr 18: |
Lecture 26
Class review |
|
Week 15
| Apr 23: |
Lecture 27
Project consultation |
|
| Apr 25: |
Lecture 28
Project consultation |
|
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