Schedule
-
EventDateDescriptionCourse Material
-
Lecture01/12/2023
ThursdayLecture 1 Course Introduction[Slides] -
Lecture01/17/2023
TuesdayLecture 2 Python IntroductionSuggested Readings:
-
Lecture01/17/2023
TuesdayLecture 3 Data characteristics and quality[Slides] -
Assignment01/24/2023
TuesdayHomework 1 released! -
Lecture01/24/2023
TuesdayLecture 4 Similarities and distances[Slides] -
Lecture01/26/2023
ThursdayLecture 5 Data Preprocessing[Slides] -
Lecture01/31/2023
TuesdayLecture 6 ReviewSuggested Readings:
-
Lecture02/02/2023
ThursdayLecture 7 Rule Based Classification[Slides] -
Lecture02/07/2023
TuesdayLecture 8 Decision Trees I[Slides] -
Lecture02/09/2023
ThursdayLecture 9 Decision Trees II[Slides]Suggested Readings:
-
Due02/09/2023 23:59
ThursdayHomework 1 due -
Lecture02/14/2023
TuesdayLecture 10 Classifier evaluation and overfitting[Slides] -
Lecture02/28/2023
TuesdayLecture 14 Classifier evaluation and KNN -
Lecture03/09/2023
ThursdayLecture 17 Imbalanced ClassesSuggested Readings:
-
Lecture03/21/2023
TuesdayLecture 18 Support Vector Machines[Notes] -
Lecture03/23/2023
ThursdayLecture 19 Support Vector Machines II[Notes] -
Lecture03/28/2023
TuesdayLecture 20 Support Vector Machines IIISuggested Readings:
-
Lecture04/04/2023
TuesdayLecture 22 Ensemble Models I (Bagging)[Notes]Suggested Readings:
-
Lecture04/06/2023
ThursdayLecture 23 Ensemble Models II (Boosting)Suggested Readings:
-
Lecture04/18/2023
TuesdayLecture 24 K-means clustering[Notes]Suggested Readings: