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Lecture 15: Data Cleaning for ML

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Presentation on theme: "Lecture 15: Data Cleaning for ML"— Presentation transcript:

1 Lecture 15: Data Cleaning for ML

2 Announcements Intermediate report due right after spring break: 4/3
Required elements: writing.html Introduction Related work Outline of contribution and technique Experimental setup Project meetings next week Send me s if you want to meet

3 Today’s Agenda Recap on ML models Training under noise

4 Section 1 1. Recap on ML models

5 What is ML all about? Minimization of a modular loss
Section 1 What is ML all about? Minimization of a modular loss Example for a linear model

6 Gradient Descent [Cauchy 1847]
Section 1 Gradient Descent [Cauchy 1847]

7 Section 1 Stochastic Methods

8 Convergence Rate and Computational Complexity
Section 1 Convergence Rate and Computational Complexity

9 Section 2 2. Training under noise

10 What is the problem with noise?
Section 2 What is the problem with noise? Optimizing for data obtained by a different distribution. Empirical risk is different.

11 How can we deal with noise?
Section 2 How can we deal with noise?

12 How can we deal with noise?
Section 2 How can we deal with noise?

13 Section 2 Model update

14 Estimating the gradient
Section 2 Estimating the gradient

15 Detecting Dirty Data Detector returns: Whether a record is dirty
Section 2 Detecting Dirty Data Detector returns: Whether a record is dirty And if it is dirty, which attributes have errors Enumerate set of records that violate at least one rule: Clean data = union between the set of clean data and records that satisfy all rules Dirty = violate at least one rule Adaptive methods for detection = train a classifier

16 Selecting which records to clean
Section 2 Selecting which records to clean Sampling problem Minimize the variance of the sampled gradient Use a detector to estimate cleaned values

17 Selecting which records to clean
Section 2 Selecting which records to clean Estimator: Estimate clean gradient using the dirty gradient and previous cleaning actions Linear approximation of gradient: uses average change of each feature value

18 Section 3 Strengths? Weaknesses? Discussion time!

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