UGBA 198 at UC Berkeley
Machine Learning for Business Decisions
Lectures: TBD
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Week 1 Overview
Ordinary Least Squares
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: Principal Component Analysis
- Note 7a: MAP Estimation and Decision Trees
- Note 7b: K-Nearest Neighbors, K-Means
- Note 8a: Decision Trees (cont.), Classification
- Note 8b: Linear Discriminant Analysis
- Note 9a: Classification (cont.), Support Vector Machines
- Note 9b: Support Vector Machines (from CS 189)
- Note 10: Neural Networks
- Note 11: Latent Dirichlet Allocation
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