# Difference between revisions of "Main Page"

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− | + | __NOTOC__ | |

+ | =Statistical and Discrete Methods for Scientific Computing= | ||

− | + | The [[2014 Concepts Study Page]] lists all the questions that may be asked in the oral exam. | |

− | == | + | You can generate your own practice exam at the [http://wpressutexas.net/examgenerator.php 2014 Practice Exam Machine]. |

− | * [// | + | |

− | + | ==Note on math rendering:== | |

− | + | ||

− | + | This site now uses only MathJax for math rendering. This is the most foolproof method and should | |

+ | work on all browsers. However, page loads are sometimes slow. Be patient. Your reward on a slow page | ||

+ | will be a page with lots of math! | ||

+ | |||

+ | ===CSE383M (65280) and CS395T (53715), Spring 2014=== | ||

+ | Welcome to the course! The instructor is Professor William Press (Bill), and the TA is Jeff Hussmann (Jeff). We meet Mondays and Wednesdays, 1:30 - 3:00 p.m. in CBA 4.344 with Bill, and Fridays, 1:30 - 3:00 p.m. in CBA 4.348 with Jeff. The course is aimed at first or second year graduate students, especially in the CSEM, CS, and ECE programs, but others are welcome. You'll need math at the level of <i>at least</i> 2nd year calculus, plus linear algebra, plus either more continuous math (e.g., CSEM students) or more discrete math (e.g., CS and ECE students). You'll also need to be able to program in some known computer language. | ||

+ | |||

+ | ===Mechanics of the Course=== | ||

+ | The last two years, we have tried the experiment of a "flipped" course. This has worked so well that we are doing this again this year. "Flipped" means that the lectures are all on the web as recorded webcasts. You <b>must</b> watch the assigned webcasts <b>before</b> the class for which they are scheduled; maybe watch them more than once if there are parts that you don't easily understand. Then, you will be ready for the active learning that we do in class. The class activities will <b>not</b> "cover the material". Rather, class is supposed to be for "aha moments" and for "fixing" the material in your learning memory. We'll thus do various kinds of "active learning" activities that will test and improve your understanding of the material in the lecture. Such in-class activities, often done in <i>randomized</i> groups of two or three, may include | ||

+ | * group computer programming exercises | ||

+ | * group working of problems | ||

+ | * group writing assignments | ||

+ | * discussing concepts (and communicating ideas back to the whole class) | ||

+ | * "quiz show" style activities | ||

+ | * short surprise quizzes (generally at the beginning of class -- no makeups allowed) | ||

+ | * whatever else we all think of | ||

+ | |||

+ | ===Problems for Each Segment=== | ||

+ | Every lecture segment home page has one or two relatively easy "skill" problems. You should work these after watching the segment, before class. (You might be asked to discuss your solution with your small group in class.) Also on the segment's page are one or two concept thought problems. One or another of these will sometimes be the basis of in-class activities, so you might want to think about them before class. | ||

+ | |||

+ | ===Student Wiki Pages=== | ||

+ | Every student will have a wiki page (and as many linked pages as you want). You can post your solutions to as many problems as you wish to your wiki page. You can do this either before the relevant class or afterwards. You can also make up, and solve, additional problems. Problems won't be individually graded. However, at the end of the course, the completeness and quality of you wiki page(s) will be a part of your course grade. Your wiki page can include discussion of the thought problems, as well as the skill problems. | ||

+ | |||

+ | You can also post signed comments on any other student's wiki pages. To the extent that these are generally helpful, they will add credit to your reputation and for your grade. | ||

+ | |||

+ | [[Student Pages]] | ||

+ | |||

+ | ===Laptops or Tablets=== | ||

+ | You <b>must</b> bring your laptop computer or full-sized tablet to every class, so that you can (i) look things up during group discussions or problem sessions and (ii) do in-class programming exercises. You can program in any language you want. For Python, which we recommend as the best choice for this course, you can either install it on your machine, or else use the IPython notebook server described in class. The course will include several lectures of Python workshop by Jeff. | ||

+ | |||

+ | If you instead want to use MATLAB or Mathematica, that is fine, but please be sure that it is installed on your computer before the first class. (The MATLAB Student Edition is a real bargain.) For C, C++, Java, etc., please be sure that you have a fully working environment for compiling and running small pieces of code. | ||

+ | |||

+ | ===Course Requirements and Grading=== | ||

+ | Grades will be based on these factors | ||

+ | * in-class attendance and participation | ||

+ | * an in-class midterm exam | ||

+ | * completeness and quality of your individual wiki page(s) | ||

+ | * relevance and usefulness of your comments on other people's wiki pages (or on the main wiki) | ||

+ | * an individual 30-minute final oral exam | ||

+ | |||

+ | In previous years there was a term project, but not this year. Your working the problems and posting solutions on your wiki page is this year's substitute. | ||

+ | |||

+ | [[File:learning_cone.gif|200px|thumb|right|Click image to see a legible version.]] | ||

+ | |||

+ | ===What is Active Learning?=== | ||

+ | Much research shows that lecture courses, where students listen passively as the instructor talks, are inefficient ways to learn. What works is so-called [http://en.wikipedia.org/wiki/Active_learning active learning], a broad term that, for us, basically means that class time is too valuable to waste on lectures. (See image at right.) | ||

+ | |||

+ | The lectures are all recorded as webcasts, but webcasts are not active learning. However, they are a starting point as a "linear" introduction to the material. | ||

+ | |||

+ | ===Feedback=== | ||

+ | |||

+ | What has worked well in class so far? What hasn't worked? How could things be improved? Please leave [[Feedback 2014|feedback]]. | ||

+ | |||

+ | ===Resources and Links=== | ||

+ | |||

+ | There is no textbook for the course. A list of recommended supplementary books is [[Recommended books|here]]. | ||

+ | |||

+ | Some resources for learning Python can be found [[Python resources|here]]. | ||

+ | |||

+ | Some MATLAB resources can be found [[MATLAB resources|here]]. | ||

+ | |||

+ | ===Webcast Lecture Segments <i>(Opinionated Lessons in Statistics)</i>=== | ||

+ | All of the lectures are in the form of webcasts, divided into segments of about 15-30 minutes each (occasionally a bit longer). Each segment, has a wiki page, page links below. You can view the lecture on its wiki page, which also has additional stuff about the segment (including the <b>skill and thought problems</b>, or by clicking directly to YouTube, where they are all on Bill's [http://www.youtube.com/user/opinionatedlessons/videos?view=0&flow=list&sort=da "Opinionated Lessons" channel]. | ||

+ | |||

+ | <center> | ||

+ | {| class="wikitable" | ||

+ | |+Watch segments BEFORE class on the indicated dates: | ||

+ | |- | ||

+ | |Mon Jan 13 | ||

+ | |<b>First Day of Class</b> (no segment due) | ||

+ | |- | ||

+ | |Wed Jan 15 | ||

+ | |[[Segment 1. Let's Talk about Probability]] (or [http://www.youtube.com/watch?v=H5WjVgL6Nh4 YouTube]) | ||

+ | |- | ||

+ | |Fri Jan 17 | ||

+ | |[[Python Set-up Tutorial and Workshop]] (no segment due) | ||

+ | |- | ||

+ | |Mon Jan 20 | ||

+ | |<b>Martin Luther King Day HOLIDAY</b> (no segment due) | ||

+ | |- | ||

+ | |Wed Jan 22 | ||

+ | |[[Segment 2. Bayes]] (or [http://www.youtube.com/watch?v=FROAk4AFKHk YouTube]) | ||

+ | |- | ||

+ | |Fri Jan 24 | ||

+ | |[[Segment 3. Monty Hall]] (or [http://www.youtube.com/watch?v=Rxb8JG8nUFA YouTube]) | ||

+ | |- | ||

+ | |Mon Jan 27 | ||

+ | |[[Segment 4. The Jailer's Tip]] (or [http://www.youtube.com/watch?v=425D0CjLLLs YouTube]) | ||

+ | |- | ||

+ | |Wed Jan 29 | ||

+ | |[[Segment 5. Bernoulli Trials]] (or [http://www.youtube.com/watch?v=2T3KP2LleFg YouTube]) | ||

+ | |- | ||

+ | |Fri Jan 31 | ||

+ | |[[Segment 6. The Towne Family Tree]] (or [http://www.youtube.com/watch?v=y_L2THpv5Jg YouTube]) | ||

+ | |- | ||

+ | |Mon Feb 3 | ||

+ | |[[Segment 7. Central Tendency and Moments]] (or [http://www.youtube.com/watch?v=ZWOmsKWQ7Fw YouTube]) | ||

+ | |- | ||

+ | |Wed Feb 5 | ||

+ | |[[Segment 8. Some Standard Distributions]] (or [http://www.youtube.com/watch?v=EDYDC7iNGTg YouTube]) | ||

+ | |- | ||

+ | |Fri Feb 7 | ||

+ | |[[Segment 9. Characteristic Functions]] (or [http://www.youtube.com/watch?v=NJL-BX6HuxY YouTube]) | ||

+ | |- | ||

+ | |Mon Feb 10 | ||

+ | |[[Segment 10. The Central Limit Theorem]] (or [http://www.youtube.com/watch?v=IpuYGsKplSw YouTube]) | ||

+ | |- | ||

+ | |Wed Feb 12 | ||

+ | |[[Segment 11. Random Deviates]] (or [http://www.youtube.com/watch?v=4r1GlyisB8E YouTube]) | ||

+ | |- | ||

+ | |Fri Feb 14 | ||

+ | |[[Segment 12. P-Value Tests]] (or [http://www.youtube.com/watch?v=2Ul7TI0B5ek YouTube]) | ||

+ | |- | ||

+ | |Mon Feb 17 | ||

+ | |[[Segment 13. The Yeast Genome]] (or [http://www.youtube.com/watch?v=QSgUX-Do8Tc YouTube]) | ||

+ | |- | ||

+ | |Wed Feb 19 | ||

+ | |[[Segment 14. Bayesian Criticism of P-Values]] (or [http://www.youtube.com/watch?v=IKV6Pn18C7o YouTube]) | ||

+ | |- | ||

+ | |Fri Feb 21 | ||

+ | |[[Segment 16. Multiple Hypotheses]] (or [http://www.youtube.com/watch?v=w6AjduOEN2k YouTube]) [note order!] | ||

+ | |- | ||

+ | |Mon Feb 24 | ||

+ | |[[Segment 15. The Towne Family - Again]] (or [http://www.youtube.com/watch?v=Y-i0CN15X-M YouTube]) [note order!] | ||

+ | |- | ||

+ | |Wed Feb 26 | ||

+ | |[[Segment 17. The Multivariate Normal Distribution]] (or [http://www.youtube.com/watch?v=t7Z1a_BOkN4 YouTube]) | ||

+ | |- | ||

+ | |Fri Feb 28 | ||

+ | |[[Review Session for Mid-Term Exam]] (no new segment due) | ||

+ | |- | ||

+ | |} | ||

+ | |||

+ | <b>Monday, March 3. MIDTERM EXAM</b> | ||

+ | |||

+ | [[Media:Midterm.pdf|(Exam)]] [[Media:MidtermSolutions2014.pdf|(Bill's solutions)]] [[Media:MidtermHistogram.pdf|(Histogram of grades)]] | ||

+ | |||

+ | {| class="wikitable" | ||

+ | |Wed Mar 5 | ||

+ | |[[Segment 18. The Correlation Matrix]] (or [http://www.youtube.com/watch?v=aW5q_P0it9E YouTube]) | ||

+ | |- | ||

+ | |Fri Mar 7 | ||

+ | |[[Segment 19. The Chi Square Statistic]] (or [http://www.youtube.com/watch?v=87EMhmPkOhk YouTube]) | ||

+ | |- | ||

+ | |} | ||

+ | |||

+ | <b>Monday, March 10 through Friday, March 14: SPRING BREAK</b> | ||

+ | |||

+ | {| class="wikitable" | ||

+ | |+Watch segments BEFORE class on the indicated dates: | ||

+ | |Mon Mar 17 | ||

+ | |[[Segment 20. Nonlinear Least Squares Fitting]] (or [http://www.youtube.com/watch?v=xtBCGPHRcb0 YouTube]) | ||

+ | |- | ||

+ | |Wed Mar 19 | ||

+ | |[[Segment 21. Marginalize or Condition Uninteresting Fitted Parameters]] (or [http://www.youtube.com/watch?v=yxZUS_BpEZk YouTube]) | ||

+ | |- | ||

+ | |Fri Mar 21 | ||

+ | |[[Segment 22. Uncertainty of Derived Parameters]] (or [http://www.youtube.com/watch?v=ZoD3_rov--w YouTube]) | ||

+ | |- | ||

+ | |Mon Mar 24 | ||

+ | |[[Segment 23. Bootstrap Estimation of Uncertainty]] (or [http://www.youtube.com/watch?v=1OC9ul-1PVg YouTube]) | ||

+ | |- | ||

+ | |Wed Mar 26 | ||

+ | |[[Segment 24. Goodness of Fit]] (or [http://www.youtube.com/watch?v=EJleSVf0Z-U YouTube]) | ||

+ | |- | ||

+ | |Fri Mar 28 | ||

+ | |[[Segment 27. Mixture Models]] (or [http://www.youtube.com/watch?v=9pWnZcpYh44 YouTube]) | ||

+ | |- | ||

+ | |Mon Mar 31 | ||

+ | |[[Segment 28. Gaussian Mixture Models in 1-D]] (or [http://www.youtube.com/watch?v=n7u_tq0I6jM YouTube]) | ||

+ | |- | ||

+ | |Wed Apr 2 | ||

+ | |[[Segment 29. GMMs in N-Dimensions]] (or [http://www.youtube.com/watch?v=PH8_qqDTCYY YouTube]) | ||

+ | |- | ||

+ | |Fri Apr 4 | ||

+ | |[[Segment 30. Expectation Maximization (EM) Methods]] (or [http://www.youtube.com/watch?v=StQOzRqTNsw YouTube]) | ||

+ | |- | ||

+ | |Mon Apr 7 | ||

+ | |[[Segment 31. A Tale of Model Selection]] (or [http://www.youtube.com/watch?v=_G1gzqQzbuM YouTube]) | ||

+ | |- | ||

+ | |Wed Apr 9 | ||

+ | |[[Segment 32. Contingency Tables: A First Look]] (or [http://www.youtube.com/watch?v=NvCdN2RFufY YouTube]) | ||

+ | |- | ||

+ | |Fri Apr 11 | ||

+ | |[[Segment 33. Contingency Table Protocols and Exact Fisher Test]] (or [http://www.youtube.com/watch?v=9Qrkw5UfAmQ You Tube]) | ||

+ | |- | ||

+ | |Mon Apr 14 | ||

+ | |[[Segment 34. Permutation Tests]] (or [http://www.youtube.com/watch?v=_4BUS1NGNHA YouTube]) | ||

+ | |- | ||

+ | |Wed Apr 16 | ||

+ | |[[Segment 37. A Few Bits of Information Theory]] (or [http://www.youtube.com/watch?v=ktzYOLDN3u4 YouTube]) | ||

+ | |- | ||

+ | |Fri Apr 18 | ||

+ | |[[Segment 38. Mutual Information]] (or [http://www.youtube.com/watch?v=huNPh1mkJHM YouTube]) | ||

+ | |- | ||

+ | |Mon Apr 21 | ||

+ | |[[Segment 39. MCMC and Gibbs Sampling ]] (or [http://www.youtube.com/watch?v=4gNpgSPal_8 YouTube]) | ||

+ | |- | ||

+ | |Wed Apr 23 | ||

+ | |[[Segment 40. Markov Chain Monte Carlo, Example 1 ]] (or [http://www.youtube.com/watch?v=nSKZ02ZWzsY YouTube]) | ||

+ | |- | ||

+ | |Fri Apr 25 | ||

+ | |[[Segment 41. Markov Chain Monte Carlo, Example 2 ]] (or [http://www.youtube.com/watch?v=FnNckBLWJ24 YouTube]) | ||

+ | |- | ||

+ | |Mon Apr 28 | ||

+ | |[[Segment 47. Low-Rank Approximation of Data ]] (or [http://www.youtube.com/watch?v=M0gsHNS_5FE YouTube])<br> | ||

+ | |- | ||

+ | |Wed Apr 30 | ||

+ | |[[Segment 48. Principal Component Analysis (PCA)]] (or [http://www.youtube.com/watch?v=frWqIUpIxLg YouTube]) | ||

+ | |- | ||

+ | |Fri May 2 | ||

+ | | <b>Review Session for Oral Exams</b> | ||

+ | |} | ||

+ | |||

+ | <b>Monday, May 5 and Tuesday, May 6: ORAL FINAL EXAMS</b> | ||

+ | |||

+ | </center> | ||

+ | |||

+ | ===Extra Credit Segments (segment number indicates intended sequence)=== | ||

+ | [[Segment 25. Fitting Models to Counts]] (or [http://www.youtube.com/watch?v=YXaq2PVCGZQ YouTube])<br> | ||

+ | [[Segment 26. The Poisson Count Pitfall]] (or [http://www.youtube.com/watch?v=rPO3N5GI-3I YouTube])<br> | ||

+ | [[Segment 35. Ordinal vs. Nominal Contingency Tables]] (or [http://www.youtube.com/watch?v=fYUbj78aguk YouTube])<br> | ||

+ | [[Segment 36. Contingency Tables Have Nuisance Parameters]] (or [http://www.youtube.com/watch?v=bHK79WKOX-Y YouTube])<br> | ||

+ | [[Segment 49. Eigenthingies and Main Effects]] (or [http://www.youtube.com/watch?v=LpGQnvvGLMQ YouTube])<br> | ||

+ | |||

+ | ===Segments with Slides But Not Yet Recorded=== | ||

+ | (links are to PDF files) | ||

+ | |||

+ | [http://wpressutexas.net/coursefiles/15.5.PoissonProcessesOrderStatistics.pdf Segment 15.5. Poisson Processes and Order Statistics]<br> | ||

+ | [http://wpressutexas.net/coursefiles/42.WienerFiltering.pdf Segment 42. Wiener Filtering]<br> | ||

+ | [http://wpressutexas.net/coursefiles/43.TheIRELady.pdf Segment 43. The IRE Lady]<br> | ||

+ | [http://wpressutexas.net/coursefiles/44.Wavelets.pdf Segment 44. Wavelets]<br> | ||

+ | [http://wpressutexas.net/coursefiles/45.LaplaceInterpolation.pdf Segment 45. Laplace Interpolation]<br> | ||

+ | [http://wpressutexas.net/coursefiles/46.InterpolationOnScatteredData.pdf Segment 46. Interpolation On Scattered Data]<br> | ||

+ | [http://wpressutexas.net/coursefiles/50.BinaryClassifiers.pdf Segment 50. Binary Classifiers]<br> | ||

+ | [http://wpressutexas.net/coursefiles/51.HierarchicalClassification.pdf Segment 51. Hierarchical Classification]<br> | ||

+ | [http://wpressutexas.net/coursefiles/52.DynamicProgramming.pdf Segment 52. Dynamic Programming]<br> | ||

+ | |||

+ | ===Team Randomizer=== | ||

+ | Link to [http://wpressutexas.net/coursefiles/teamrandomizer.php the team randomizer] |

## Latest revision as of 17:48, 24 January 2019

# Statistical and Discrete Methods for Scientific Computing

The 2014 Concepts Study Page lists all the questions that may be asked in the oral exam.

You can generate your own practice exam at the 2014 Practice Exam Machine.

## Note on math rendering:

This site now uses only MathJax for math rendering. This is the most foolproof method and should work on all browsers. However, page loads are sometimes slow. Be patient. Your reward on a slow page will be a page with lots of math!

### CSE383M (65280) and CS395T (53715), Spring 2014

Welcome to the course! The instructor is Professor William Press (Bill), and the TA is Jeff Hussmann (Jeff). We meet Mondays and Wednesdays, 1:30 - 3:00 p.m. in CBA 4.344 with Bill, and Fridays, 1:30 - 3:00 p.m. in CBA 4.348 with Jeff. The course is aimed at first or second year graduate students, especially in the CSEM, CS, and ECE programs, but others are welcome. You'll need math at the level of *at least* 2nd year calculus, plus linear algebra, plus either more continuous math (e.g., CSEM students) or more discrete math (e.g., CS and ECE students). You'll also need to be able to program in some known computer language.

### Mechanics of the Course

The last two years, we have tried the experiment of a "flipped" course. This has worked so well that we are doing this again this year. "Flipped" means that the lectures are all on the web as recorded webcasts. You **must** watch the assigned webcasts **before** the class for which they are scheduled; maybe watch them more than once if there are parts that you don't easily understand. Then, you will be ready for the active learning that we do in class. The class activities will **not** "cover the material". Rather, class is supposed to be for "aha moments" and for "fixing" the material in your learning memory. We'll thus do various kinds of "active learning" activities that will test and improve your understanding of the material in the lecture. Such in-class activities, often done in *randomized* groups of two or three, may include

- group computer programming exercises
- group working of problems
- group writing assignments
- discussing concepts (and communicating ideas back to the whole class)
- "quiz show" style activities
- short surprise quizzes (generally at the beginning of class -- no makeups allowed)
- whatever else we all think of

### Problems for Each Segment

Every lecture segment home page has one or two relatively easy "skill" problems. You should work these after watching the segment, before class. (You might be asked to discuss your solution with your small group in class.) Also on the segment's page are one or two concept thought problems. One or another of these will sometimes be the basis of in-class activities, so you might want to think about them before class.

### Student Wiki Pages

Every student will have a wiki page (and as many linked pages as you want). You can post your solutions to as many problems as you wish to your wiki page. You can do this either before the relevant class or afterwards. You can also make up, and solve, additional problems. Problems won't be individually graded. However, at the end of the course, the completeness and quality of you wiki page(s) will be a part of your course grade. Your wiki page can include discussion of the thought problems, as well as the skill problems.

You can also post signed comments on any other student's wiki pages. To the extent that these are generally helpful, they will add credit to your reputation and for your grade.

### Laptops or Tablets

You **must** bring your laptop computer or full-sized tablet to every class, so that you can (i) look things up during group discussions or problem sessions and (ii) do in-class programming exercises. You can program in any language you want. For Python, which we recommend as the best choice for this course, you can either install it on your machine, or else use the IPython notebook server described in class. The course will include several lectures of Python workshop by Jeff.

If you instead want to use MATLAB or Mathematica, that is fine, but please be sure that it is installed on your computer before the first class. (The MATLAB Student Edition is a real bargain.) For C, C++, Java, etc., please be sure that you have a fully working environment for compiling and running small pieces of code.

### Course Requirements and Grading

Grades will be based on these factors

- in-class attendance and participation
- an in-class midterm exam
- completeness and quality of your individual wiki page(s)
- relevance and usefulness of your comments on other people's wiki pages (or on the main wiki)
- an individual 30-minute final oral exam

In previous years there was a term project, but not this year. Your working the problems and posting solutions on your wiki page is this year's substitute.

### What is Active Learning?

Much research shows that lecture courses, where students listen passively as the instructor talks, are inefficient ways to learn. What works is so-called active learning, a broad term that, for us, basically means that class time is too valuable to waste on lectures. (See image at right.)

The lectures are all recorded as webcasts, but webcasts are not active learning. However, they are a starting point as a "linear" introduction to the material.

### Feedback

What has worked well in class so far? What hasn't worked? How could things be improved? Please leave feedback.

### Resources and Links

There is no textbook for the course. A list of recommended supplementary books is here.

Some resources for learning Python can be found here.

Some MATLAB resources can be found here.

### Webcast Lecture Segments *(Opinionated Lessons in Statistics)*

All of the lectures are in the form of webcasts, divided into segments of about 15-30 minutes each (occasionally a bit longer). Each segment, has a wiki page, page links below. You can view the lecture on its wiki page, which also has additional stuff about the segment (including the **skill and thought problems**, or by clicking directly to YouTube, where they are all on Bill's "Opinionated Lessons" channel.

**Monday, March 3. MIDTERM EXAM**

(Exam) (Bill's solutions) (Histogram of grades)

Wed Mar 5 | Segment 18. The Correlation Matrix (or YouTube) |

Fri Mar 7 | Segment 19. The Chi Square Statistic (or YouTube) |

**Monday, March 10 through Friday, March 14: SPRING BREAK**

**Monday, May 5 and Tuesday, May 6: ORAL FINAL EXAMS**

### Extra Credit Segments (segment number indicates intended sequence)

Segment 25. Fitting Models to Counts (or YouTube)

Segment 26. The Poisson Count Pitfall (or YouTube)

Segment 35. Ordinal vs. Nominal Contingency Tables (or YouTube)

Segment 36. Contingency Tables Have Nuisance Parameters (or YouTube)

Segment 49. Eigenthingies and Main Effects (or YouTube)

### Segments with Slides But Not Yet Recorded

(links are to PDF files)

Segment 15.5. Poisson Processes and Order Statistics

Segment 42. Wiener Filtering

Segment 43. The IRE Lady

Segment 44. Wavelets

Segment 45. Laplace Interpolation

Segment 46. Interpolation On Scattered Data

Segment 50. Binary Classifiers

Segment 51. Hierarchical Classification

Segment 52. Dynamic Programming

### Team Randomizer

Link to the team randomizer