'Coin flips' presentation slideshows

Introduction to Machine Learning

Introduction to Machine Learning. Manik Varma Microsoft Research India http://research.microsoft.com/~manik manik@microsoft.com. Binary Classification. Is this person Madhubala or not? Is this person male or female? Is this person beautiful or not?. Multi-Class Classification.

1.3k views • 110 slides

Chapter 1

Chapter 1 . Data Collection. Section 1.1. Introduction to the Practice of Statistics. Statistics. The science of statistics is Collecting Organizing Summarizing Analyzing information to draw conclusions or answer questions Statistics provides a measure of confidence in any conclusion.

1.1k views • 75 slides

Cognitive Biases 4

Cognitive Biases 4. Fallacies Involving Probability. Conjunction Fallacy.

992 views • 71 slides

Cognitive Biases 4

Cognitive Biases 4. Conjunction Fallacy. Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable?

644 views • 48 slides

MF-852 Financial Econometrics

612 views • 45 slides

How much sleep did you get last night?

How much sleep did you get last night?. <6 6 7 8 9 >9. Upcoming In Class. Thursday Quiz 2, covers HW2 and HW3 of the material learned in class. (9/12) Sunday HW4 (9/16) Part 2 of the data project due next Monday (9/17). Chapter 14. From Randomness to Probability.

440 views • 33 slides

Chapter 16

Chapter 16. For Wednesday, January 31. Read over chapter 22 Make sure you Bring text. January 28 Outline. 1.5 Scientific Method 16.3 Darwin Principles 16.5 Individuals do not evolve, populations do 16.6 Mutations 16.7 Directional Selection

412 views • 33 slides

CS151 Complexity Theory

CS151 Complexity Theory. Lecture 14 May 13, 2004. Outline. accidentally omitted material on PH non-uniformity and the PH BPP and the PH resume interactive proofs and their power. Karp-Lipton. we know that P = NP implies SAT has polynomial-size circuits.

416 views • 31 slides

Class 02 Probability, Probability Distributions, Binomial Distribution

471 views • 29 slides

Class 02 Probability, Probability Distributions, Binomial Distribution

419 views • 29 slides

Statistics

380 views • 29 slides

Sutton & Barto , Chapter 4

Sutton & Barto , Chapter 4. Dynamic Programming. Programming Assignments? Course Discussions?. Review: V, V* Q, Q* ?, ?* Bellman Equation vs. Update. Solutions Given a Model. Finite MDPs Exploration / Exploitation? Where would Dynamic Programming be used?.

495 views • 25 slides

Probabilistic analysis

400 views • 23 slides

Introduction to summary statistics: Sample mean & sample variance

Fred Boehm Statistics 224 January 27, 2014. Introduction to summary statistics: Sample mean & sample variance. 224 logistics. Website updates: Revised office hours info Alyssa & Huikun: 12-2pm today in MSC 1217c Fred: 6:30-8pm today in Wendt library (room 129) Electronic survey

352 views • 17 slides

OUTLINE 12 IV. Mendel's Work D. The dihybrid cross 1. qualitative results

OUTLINE 12 IV. Mendel's Work D. The dihybrid cross 1. qualitative results 2. quantitative results E. Summary of Mendel's Rules V. Probability Theory and Patterns of Inheritance A. Definitions B. Rules for probability 1. independent outcomes 2. the product rule

246 views • 16 slides

A Discrete Probability Distribution: The Binomial Distribution

A Discrete Probability Distribution: The Binomial Distribution. MSIT 3000 Lecture 06. Objectives:. Recognize situations in which it is appropriate to use a binomial distribution. Be able to use the binomial distribution to calculate probabilities.

651 views • 15 slides

Machine Learning Saarland University, SS 2007

246 views • 13 slides

Machine Learning Saarland University, SS 2007

242 views • 13 slides

Introduction to Binomial Distributions

Introduction to Binomial Distributions. Presentation 5.1 Overview and Examples. Binomial Distributions. A binomial distribution or setting is simply the total number of successes in a sequence of n trials. The typical example is the number of heads in a sequence of n coin flips.

812 views • 8 slides

By Franklin Kramer

Variables Affecting Participant Honesty in Experimental Design: Further Manipulations of Coin Flipping in Mechanical Turk. By Franklin Kramer. What is Mechanical Turk?. Crowdsourcing web service Have turkers complete HITs for small amounts of money (most being 1-25 cents)

155 views • 6 slides