Schedule

Contents

Schedule#

Warning

As this is a new course, the schedule may change throughout the semester to best accommodate your learning!

Topics#

A list of topics we will cover can be found below. Note that topics may be subject to change.

  • Regression

  • Gradient descent

  • Classification

  • Model selection and evaluation

  • Regularization

  • Feature engineering

  • Fairness and interpretability

  • Ensembling

  • Neural networks

  • Unsupervised learning

    • K-means clustering

    • Autoencoders

Monday

Tuesday

Wednesday

Thursday

Friday

1/26

1/27
Introduction

Survey 1

1/28

1/29
kNN, Math, NumPy

Worksheet 1

1/30

2/2
Survey 1

2/3
Linear Regression

2/4
Worksheet 1

2/5
Gradient Descent I

Homework 1

2/6

Homework 1

2/9

2/10
Gradient Descent II

2/11

2/12
Model evaluation

2/13
Homework 1

2/16
Homework 1

2/17
Regularization

Worksheet 2

2/18

2/19
Classification I

2/20

2/23
Worksheet 2

2/24
Classification II

Homework 2

2/25

2/26
Classification Metrics

2/27

3/2

3/3
ROC Curves

3/4
Homework 2

3/5
Fairness

Survey 2

3/6
Worksheet 3

3/9

3/10
Trees and Ensembling I

3/11
Worksheet 3

3/12
Ensembling II

Final project proposal

3/13
Worksheet 3
Survey 2

03/16 - 3/22
🌼 Spring break 🌸