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Course Information

Course Learning Objectives

Notes

Course Introduction
Lecture 1: Terminology, Baselines, Decision Trees
Lecture 2: Machine Learning Fundamentals
Lecture 3: \(k\)-Nearest Neighbours and SVM RBFs
Lecture 4: Preprocessing, sklearn Pipeline, sklearn ColumnTrasnsformer
Lecture 5: More preprocessing, text features
Lecture 6: Hyperparameter Optimization and Optimization Bias
Lecture 7: Naive Bayes
Lecture 8: Linear Models
Appendix A: Multi-class, meta-strategies

Class Demos

Lecture 2: Class demo
Lecture 3: Class demo
Lecture 5: Class demo
Lectures 5: Class demo
Lectures 6: Class demo
Lecture 7: Class demo
Lectures 8: Class demo

Section slides

Section 101 Classifiers
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
Section 102 Regressors
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8

Attribution

Attributions
LICENSE

.md

Section 101 Classifiers

Section 101 Classifiers#

Click here to access DSCI Section 101 lecture slides..

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Lectures 8: Class demo

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Section 102 Regressors

By Varada Kolhatkar

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