UGBA 198 at UC Berkeley

# Machine Learning for Business Decisions

Lectures: Wednesdays from 6-8 p.m., C-320 Cheit

### Week 3 Overview

## Maximum Likelihood Estimation

## Notes

See Syllabus for more information. You can find a list of week-by-week topics.

- Note 1: Least Squares (Draft)
- Note 2: OLS, Ridge Regression
- Note 3: Gradient Descent, Feature Engineering, and Regularization
- Note 4: Regularization (cont.) and Bias-Variance Decomposition
- Note 5: MLE and MAP: Statistical Justifications
- Note 6: MAP Estimation and Decision Trees
- Note 7: Decision Trees (cont.), Classification
- Note 8: Classification (cont.), Support Vector Machines
- Note 9: Support Vector Machines (from CS 189)
- Note 10: Neural Networks
- Note 11: K-Nearest Neighbors, K-Means

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