Schedule

Event

Date

Description

Course Material
Lecture

01/12/2023
Thursday

Lecture 1 Course Introduction
[Slides]
Lecture

01/17/2023
Tuesday

Lecture 2 Python Introduction
Suggested Readings:
Lecture

01/17/2023
Tuesday

Lecture 3 Data characteristics and quality
[Slides]
Assignment

01/24/2023
Tuesday

Homework 1 released!

[Homework 1]
Lecture

01/24/2023
Tuesday

Lecture 4 Similarities and distances
[Slides]
Lecture

01/26/2023
Thursday

Lecture 5 Data Preprocessing
[Slides]
Lecture

01/31/2023
Tuesday

Lecture 6 Review
Suggested Readings:
- Correlation (Colab)
Lecture

02/02/2023
Thursday

Lecture 7 Rule Based Classification
[Slides]
Lecture

02/07/2023
Tuesday

Lecture 8 Decision Trees I
[Slides]
Lecture

02/09/2023
Thursday

Lecture 9 Decision Trees II
[Slides]
Suggested Readings:
- Decision Trees (Colab)
Due

02/09/2023 23:59
Thursday

Homework 1 due
Lecture

02/14/2023
Tuesday

Lecture 10 Classifier evaluation and overfitting
[Slides]
Lecture

02/28/2023
Tuesday

Lecture 14 Classifier evaluation and KNN
Suggested Readings:
- Model selection with generalization error (Colab)
- Model selection with K-fold CV (Colab)
Lecture

03/09/2023
Thursday

Lecture 17 Imbalanced Classes
Suggested Readings:
- Imbalanced Class (Colab)
Lecture

03/21/2023
Tuesday

Lecture 18 Support Vector Machines
[Notes]
Lecture

03/23/2023
Thursday

Lecture 19 Support Vector Machines II
[Notes]
Lecture

03/28/2023
Tuesday

Lecture 20 Support Vector Machines III
Suggested Readings:
- SVMs (Colab)
Lecture

04/04/2023
Tuesday

Lecture 22 Ensemble Models I (Bagging)
[Notes]
Suggested Readings:
- Bagging(Colab)
Lecture

04/06/2023
Thursday

Lecture 23 Ensemble Models II (Boosting)
Suggested Readings:
- Boosting (Colab)
Lecture

04/18/2023
Tuesday

Lecture 24 K-means clustering
[Notes]
Suggested Readings:
- K-means clustering (Colab)