Week	Lecture (and book chapter)	Demo
1	intro getting to know your data (chapter 2)
2	getting to know your data (chapter 2) data preprocessing (chapter 3)	colab intro panda demo
3	data reduction	pca data reduction demo
4	classification concepts, trees (Chapter 8)	normalization + decision tree demo
5		pipeline demo visualization demo streamlit demo
6	naive bayes(Chapter 8) model evaluation(Chapter 8)
7	ensemble classifiers(Chapter 8)	ensemble+cross validation+evaluation demo demo recorded video
8	support vector machine(Chapter 9) Stacking Classifier	Stacking Classifier Demo
9	Spring Break	Spring Break
10	Advanced Concepts Dataset Issues Clustering(Chapter 10)	Project start, choose dataset
11	Clustering	Clustering Demo
12	Outlier Analysis(Chapter 12), Frequent Pattern Mining(Chapter 6)
13	Pitfalls of ML, Backprop
14	Backprop(Chapter 9)
15	Backprop	project submission

Quiz Syllabus

Quiz	Syllabus	Date
Quiz 1	getting to know your data, data preprocessing, data reduction, PCA classification concepts, trees	February 9
Quiz 2	ensemble methods, naive bayes, model evaluation, dataset issues, support vector machines	March 23
Quiz 3	clustering, frequent pattern mining, outlier analysis,pitfalls of ml, backprop	April 13

Hands-on Exam Syllabus

Quiz	Syllabus	Date
Hands-on exam 1	on assignment 1 and assignment 2	February 30
Hands-on exam 2	on assignment 3 and assignment 4	April 10