# 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)**No Lab****Homework**Homework 1 (due Jan. 27)- Jan 23
**Lecture**Data Sampling and Probability (webcast)- Jan 24
**Discussion**Discussion 1 (solutions)

### Week 2

- Jan 27
**Homework**Homework 2 (due Feb. 3)- Jan 28
**Lab**Lab 1 (due Feb. 3)- Jan 30
**Lecture**Finish SQL and Start Pandas (webcast) (html1) (html2) (raw code)- Jan 31
**Discussion**Discussion 2 (solutions)

### Week 3

- Feb 3
**Homework**Homework 3 (due Feb. 10)- Feb 4
**Lab**Lab 2 (due Feb. 10)- Feb 6
**Lecture**Data Cleaning and EDA (webcast) (code)- 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)**Lab**Project 1 Office Hours (no lab assignment)- Feb 13
**Lecture**Visualization I (webcast)- Feb 14
**Discussion**Discussion 4 (notebook) (solutions)

### Week 5

- Feb 17
**Vitamin**Vitamin 2 (due Feb. 17)**Project**Project 1B (due Feb. 24)- Feb 18
**Lecture**Visualization II (webcast) (code)**Lab**Lab 3 (due Feb. 24)- Feb 20
**Lecture**Modeling and Estimation (webcast) (code)- 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)**Lab**Lab 4 (due Mar. 2)- Feb 27
**Lecture**Gradient Descent and Pytorch (webcast)- 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)**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)- 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 14

- Apr 20
**Project**Project 2B (due Apr. 27)- Apr 21
**Lecture**Decision Trees and Random Forests (Prof. Hug’s Excellent Lecture)(webcast) (web notebook, code)**Lab**Lab 10 (due Apr. 27)- Apr 23
**Lecture**Dimensionality Reduction (Prof. Hug’s Excellent Lecture)(webcast)(web notebook, code), (Optional PCA Tutorial)- Apr 24
**Discussion**Discussion 12 (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