Cs251 Spring 2016 Lecture 12

Cs251 Spring 2014 Lecture 12

1 Probability distribution functions Probability distribution functions assign probabilities to each of the possible outcomes of a random experiment. These distributions can be continuous (e.g. grading on a 4.0 scale), or discrete (e.g. grading with letter grades) (see Fig. 2). Figure 1: Terminology for continuous and discrete probability distributions. A probability distribution function speci...

Cs251 Spring 2015 Lecture 12

1 Probability distribution functions Probability distribution functions assign probabilities to each of the possible outcomes of a random experiment. These distributions can be continuous (e.g. grading on a 4.0 scale), or discrete (e.g. grading with letter grades) (see Fig. 2). Figure 1: Terminology for continuous and discrete probability distributions. A probability distribution function speci...

CS 3110 Spring 2016 Lecture 1 : Introduction , Course Plan , Elements of OCaml : Syntax , Evaluation ( 1 / 28 )

These preliminary notes contain far more material that was covered in the first lecture. A redacted copy of the notes will be posted next week. We leave these longer notes up now to help students understand better the nature of the course. The actual lecture only covered material up to OCaml syntax.

8.06 Spring 2016 Lecture Notes 3. Entanglement, density matrices and decoherence

Adaptive Data Analysis

These lecture notes are based on [BNS+16] and were compiled for a guest lecture in the course CS229r “Information Theory in Computer Science” taught by Madhu Sudan at Harvard University in Spring 2016. Menu for today’s lecture: • Motivation • Model • Overfitting & comparison to non-adaptive data analysis • What can we do adaptively? • KL Divergence recap • Proof • Differential privacy (time per...