Syllabus

Please note: This schedule is still tentative, and is likely to change. See the calendar to see the scheduling of our weekly events (discussion, lab, office hours, etc).

Week 1

Jan 21

Lecture Introduction (webcast) (code)

Ch. 1

No Lab

Homework Homework 1 (due Jan. 27)

Jan 23

Lecture Data Sampling and Probability (webcast)

Ch. 2

Jan 24

Discussion Discussion 1 (solutions)

Week 2

Jan 27

Homework Homework 2 (due Feb. 3)

Jan 28

Lecture SQL (webcast) (code)

Ch. 9

Lab Lab 1 (due Feb. 3)

Jan 30

Lecture Finish SQL and Start Pandas (webcast) (html1) (html2) (raw code)

Ch. 9, Ch. 3

Jan 31

Discussion Discussion 2 (solutions)

Week 3

Feb 3

Homework Homework 3 (due Feb. 10)

Feb 4

Lecture Pandas (webcast) (code)

Ch. 3

Lab Lab 2 (due Feb. 10)

Feb 6

Lecture Data Cleaning and EDA (webcast) (code)

Ch. 4, Ch. 5

Feb 7

Discussion Discussion 3 (notebook) (solutions)

Week 4

Feb 10

Vitamin Vitamin 1 (due Feb. 10)

Project Project 1A (due Feb. 17)

Feb 11

Lecture Regular Expressions (webcast) (code)

Ch. 8

Lab Project 1 Office Hours (no lab assignment)

Feb 13

Lecture Visualization I (webcast)

Ch. 6

Feb 14

Discussion Discussion 4 (notebook) (solutions)

Week 5

Feb 17

Vitamin Vitamin 2 (due Feb. 17)

Project Project 1B (due Feb. 24)

Ch. 6

Feb 18

Lecture Visualization II (webcast) (code)

Ch. 6

Lab Lab 3 (due Feb. 24)

Feb 20

Lecture Modeling and Estimation (webcast) (code)

Ch. 10, Ch. 13

Feb 21

Discussion Discussion 5 (solutions) (notebook)

Week 6

Feb 24

Vitamin Vitamin 3 (due Feb. 24)

Homework Homework 4 (due Mar. 2)

Feb 25

Lecture Optimization using Gradient Descent (webcast) (code) (Interactive Notebook) (Loss Game) (Bonus PyTorch Tutorial)

Ch. 11

Lab Lab 4 (due Mar. 2)

Feb 27

Lecture Gradient Descent and Pytorch (webcast)

Ch. 14

Feb 28

Discussion Discussion 6 (solutions)

Week 7

Mar 2

Vitamin Vitamin 4 (due Mar. 2)

Mar 3

Lecture Review of Modeling and Optimization, Intro to Regression (webcast)

Ch. 14

Lab Lab 5 (due Mar. 7)

Mar 5

Lecture Checkpoint Review (webcast) (Gradient Descent Code) (HTML Version)

Mar 6

Discussion Discussion 7 (solutions)

Week 8

Mar 9

Exam Checkpoint Assignment Released at 8PM (due Mar. 10, 8PM)

Mar 10

Lecture Linear Models + Geometric Interpretation (slides) (code)

NO LAB

Mar 12

Lecture Regression in SKLearn, Feature Engineering (slides) (code) (playlist)

Mar 13

Discussion Discussion 8 (solutions)

Week 9

Mar 16

Homework Homework 5 (due Mar. 30)

Mar 17

Lecture Pitfalls of Feature Engineering (code) (playlist)

Lab Lab 6 (due Mar. 30)

Mar 19

Lecture Train-Test Split and Cross Validation (slides)(code) (playlist)

Mar 20

No Discussion

Week 10 (Spring Break)

Mar 24

No Lecture

No Lab

Mar 26

No Lecture

Mar 27

No Discussion

Week 11

Mar 30

Homework Homework 6 (due Apr. 6)

Mar 31

Lecture Regularization (slides) (code) (playlist)

Lab Lab 7 (due Apr. 6)

Apr 2

Lecture Random Variables, Sampling Variability (Part 1) (Part 2) (Part 3) (video)

Apr 3

Discussion Discussion 9 (solutions) (video)

Week 12

Apr 6

Homework Homework 7 (due Apr. 13)

Apr 7

Lecture Bias Variance Tradeoff (Derivation) (video)

Lab Lab 8 (due Apr. 13)

Apr 9

Lecture Residuals, Multicollinearity, Inference (demo)

Ch. 18.3

Apr 10

Discussion Discussion 10 (solutions) (video)

Week 13

Apr 13

Homework Optional Homework (due May 11)

Project Project 2A (due Apr. 20)

Apr 14

Lecture Logistic Regression (Properties) (Part 1) (video)

Lab Lab 9 (due Apr. 20)

Apr 16

Lecture Logistic Regression Part 2 (code) (playlist)

Apr 17

Discussion Discussion 11 (solutions) (video)

Week 15

Apr 27

Final Project Final Project (due May 13) (datasets) (Undergrad Rubric) (Grad Rubric)

Homework Homework 8 (due May 4)

Apr 28

Lecture Principal Component Analysis (Prof. Hug’s Excellent Lecture)(webcast) (code)

Lab Lab 11 (due May 4)

Apr 30

Lecture Clustering (Prof. Hug) (webcast)

May 1

Discussion Discussion 13 (video)

Week 16

May 5

Lab Optional Lab (not graded)

Week 17

May 13

Final Project Due