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Proponents of the development of quantum computers state that #quantum #computer #technology could lead to breakthroughs in #self-learning #artificial #intelligence ( #AI), provide #medical insights by #simulating incredibly complex #biological molecules, manage #financial #risk at banks…

It will also be able to #simultaneously break all existing #cryptographic keys while setting the stage for #uncrackable quantum #encryption. Exactly this point is why many nations--led by the #U.S. and #China--have taken a strong #interest in quantum computing.

However, there is a wide community opposing the development of quantum computing or at least being uncertain that quantum computing will end up benefiting society...

"The great idea of constructive type theory is that there is no distinction between programs and proofs... Theorems are types (specifications), and proofs are programs" (Robert Harper) This is a…

HN Discussion: https://news.ycombinator.com/item?id=19028855

Posted by hacknrk (karma: 197)

\#HackerNews #computer #curriculum #modern #science #... Show more...

"The great idea of constructive type theory is that there is no distinction between programs and proofs... Theorems are types (specifications), and proofs are programs" (Robert Harper) This is a…

HN Discussion: https://news.ycombinator.com/item?id=19028855

Posted by hacknrk (karma: 197)

\#HackerNews #computer #curriculum #modern #science #self-learning

This is a collection of modern resources on various undergrad level computer science topics, for someone with an interest in theory. Use [1]LibGen if you canʼt buy these books. You donʼt have to do everything here, just the topics of interest to you. If for any reason you want a condensed version, watch the Great Theoretical Ideas in Computer Science lecture series to see what interests you in the field. Almost all the material in each resource is self contained so will cover necessary background which if you donʼt have see the preliminaries.

How to Learn

On Isaac Newtonʼs iteration method to self-learn geometry:

Cal Newport has some anecdotes on how he was able to get the [2]best grade in his Discrete Mathematics class, and the rest of the site is full of advice on studying, how to schedule yourself and deliberate [3]practice. Further anecdotes exist on the importance of deep work and [4]deliberate practice. There is a free [5]course Learning How to Learn on coursera.org which suggest the same techniques plus Cornell style note taking and how to read a [6]research paper.

Preliminaries

Everything here is optional, you could try just starting with CS 3110.

Intro to Computation

The [7]book How to Design Programs (HtDP) has proven success teaching an introduction to programming while remaining rigorous. One of itʼs authors is now running a public school curriculum project called [8]Bootstrap and gave a [9]talk why this style of teaching programming has the most success, and how itʼs curriculum transfers to other fields such as how the students writing games are actually writing differential equations, as the behavior of a game over time is the integral. If you really want to understand programming you should start with this book. If you liked HtDP, itʼs sequel is [10]PAPL (original [11]Racket version also exists, but Pyret and Racket are essentially the same languages just the author was tired of parenthesis complaints from other professors). A very rigorous introduction exists as the classic MIT book Structural Interpretation of Computer Programs (SICP) but the more programming you do before reading SICP, the more you get out of it.

Some Additional Optional Intros

Common Lisp Intro

Itʼs possible to do (or at least model) every course in this list using Common Lisp, such as building your own domain-specific [12]machine code while completing 15-213 Computer Systems, or hacking together [13]purely functional [14]data structures, or writing your own pattern matcher, or simulating a turing machine, ect. Of course you will do the courses using their recommended languages and tools, but in addition to this, building your own toy prototypes for the topics here in Lisp can help you learn this material as you discover it for yourself with active learning. Another shill for Common Lisp as a good language choice is it hasnʼt changed much in decades, so an old library you found that hasnʼt been updated in 15 years will still work. There is also the joy of programming Lisp as a [15]system.

Little Schemer Series

The [16]Little Schemer series books are a Q&A format/Socratic method for learning the basics of computation (read the Preface of each book). You can do the Little Schemer with pencil and paper in a weekend though the authors recommend at least 3 sittings. The first in the series is The Little Schemer which teaches you to think recursively. The second is the Seasoned Schemer covering higher-order functions, which will help understand calculus. The differential operator is a higher-order function: given a function on the real line it yields its first derivative as a function on the real line.

`\* [17](Book) The Little MLer - Matthias Felleisen, Daniel P. Friedman `

\* Self contained but will make much more sense if youʼve already done the first book, The Little Schemer covering cons, lists and recursion.

\* Teaches you to think recursively, and provides an introduction to the principles of types, computation, and program construction.

\* Applies to any functional language, almost all the exercises can be done in OCaml or another language with a small amount of syntactic adjustment.

Carnegie Mellon University

Students with direct entry to this CMU course already have a background in programming. Good information here on style, design and efficiency, with an introduction to [18]complexity otherwise you would learn all of this in HtDP.

Elementary Math

I bought George Thomasʼ Calculus and Analytical Geometry [21]3rd edition for around $12 off Thrift Books and can see why Knuth liked this book so much as Thomas carefully goes through each proof describing what is happening. The second or classic edition is the one Knuth used and was [22]reprinted recently. Thereʼs a review of trigonometry in the 3rd edition as well.

Nonexistent Math Background

The Gelfand Correspondence [23]Program in Mathematics existed in the 90s, where students were sent books and could mail back assignments to be graded and coached. These [24]books are still around and are excellent taking a problem solving approach on every page. Algebra and Trigonometry are both recommended. While you go through any of these books try a survey of The [25]Better Explained Math and Calculus series books, which explain basic math such as the natural logarithm. Gilbert Strang also has a lecture series [26]Highlights of Calculus. Cambridge also offers advice for incoming undergrads on readable [27]texts.

Weak/Forgotten Math Background

Start with [28]Po-Shen Lohʼs new site [29]Expii.com which presents topics in a brief intuitive manner, then feeds you problems in that domain and makes decisions based on your answers if you need more practice problems. The Advanced Topics have all kinds of great tutorials on CS Theory as well as Number Theory.

Calculus With Theory

Optional, Thomasʼ text will teach you the theory of calculus, though doing the exercises of Apostolʼs Calculus I is a great way to build [30]mathematical maturity. This alternative Calculus w/Theory lecture [31]series only uses a simple algebraic framework, eliminating limits/infinity. Linear algebraic ideas and the Discrete Calculus are both highly relevant to the material here. There is also a [32]full course available.

Functional Programming

The 2012 version by [33]Dan Licata has the best lecture notes, optionally combine with most [34]recent course notes. Robert Harper keeps a [35]blog and a follow up [36]post on the success of teaching this course. SML is used because itʼs syntax and module system is simple, much like Schemeʼs easy syntax was once preferred for introductory courses so the complexity of the language gets out of the way and you learn the fundamentals.

Intro to Programming using OCaml

This Cornell OCaml course is totally self contained with itʼs own [37]textbook. You will want to archive the entire course locally using [38]wget. The release code for the assignments wget them [39]here and change URL to /a4/a4.zip, /a3/a3.zip, they range a0.zip - a5.zip

`\* [40](Full Course) CS 3110 Data Structures and Functional Programming `

\* The free textbook is essentially the lectures, the [41]notes have recommended chapters for additional books like Real World Ocaml

* Introduction to Coq, you can extract OCaml from Coq, F* or from [42]Why3

\* No prereqs in programming required, if you get lost on something directly look it up in [43]this free book How to Think Like a (functional) Computer Scientist

\* See this [44]talk Effective ML how to properly write interfaces, error handling

\* You can even program hardware such as a [45]FPGA in OCaml using this [46]library

\* Or websites using [47]bucklescript

Algebra

It is possible in a functional language like ML to do algebra with types, proving two types are isomorphic with the desired properties of reflexivity, symmetry, and transitivity. Itʼs also possible to abstract Lists and Trees into polynomials, as every polynomial looks like a sum of terms. As you will learn in 15-150 Principles of Functional Programming "most functional datastructures have constant time access near the outer layer of their structure, ie: the head of a list or the root of a tree. However, access at some random point inside the structure is typically linear since looking at some element of a list is linear in the length of the list, and looking at some element of a tree is linear in the depth of the tree. Datatype derivatives allow constant time access to the entire structure."

Linear Algebra

`\* [48](Full Course) CS053 - Coding the Matrix `

\* Covers interesting applications like what was shown in Great Theoretical Ideas in Computer Science when you learned how a parity bit works.

\* No formal prerequisites except assumes you know how to do basic proofs

\* [49](Book) Linear Algebra: An Introduction to Abstract Mathematics - Robert J. Valenza

\* This free [50]book Linear Algebra - As an Introduction to Abstract Mathematics from UC Davis, is an excellent companion to Valenzaʼs text

\* These YouTube presentations [51]here help with understanding the topics on a geometric level. Watch the [52]Essence of Linear Algebra to see what this means

\* Other Resource: Sheldon Axler has [53]recorded lectures for his book Linear Algebra Done Right aka Down With Determinants!

\* The exercises in Axlerʼs book are either too easy, sometimes near impossible, but the presentation of material with vectors explained first before determinants is preferred

\* Yet Another Resource: NJ Wildberger has a Linear Algebraic Geometry series of [54]lectures

Abstract Algebra

`\* [55](Full Course) Math-371 Abstract Algebra `

\* Has recorded lectures which are essential as you can quickly get lost in the texts at this level of abstraction

\* Uses readings from [56]three texts

\* There is also this [57]Harvard course with excellent recorded lectures and uses readings from Algebra - M. Artin (blue book) as a great compliment to Math-371 lectures

\* The [58]history of Abstract Algebra by Prof Lee Lady

Discrete Mathematics

Resources to learn undergrad Discrete Mathematics.

Introduction to Pure Mathematics

`\* [59](Book) An Infinite Descent into Pure Mathematics `

\* Collection of excellent lecture notes used in CMUʼs 15-151/21-127 Concepts of Mathematics class

\* Curriculum based on findings in evidenced based teaching

\* Authorʼs motivations for not including answers are explained [60]here: "people learn more when they discover something for themselves than they do if someone tells them about it."

\* Alternatively try the [61]book Concrete Mathematics for a complete description of proof by induction, summation notation, generating functions, ect., with answers included for self-study.

Discrete Math with Standard ML

The merging of proof and program is what makes this a great book. If you canʼt get this book Discrete Mathematics, by L. Lovász, J. Pelikán, and K. Vesztergombi is normally [62]used or just skip to 15-251 Great Theoretical Ideas in CS. If you can finish this book youʼve essentially done most of 15-150 Principles of Functional Programming (such as proving programs correct) and most of 15-215 Great Theoretical Ideas in Computer Science.

`\* [63](Book/Lectures) Discrete Mathematics and Functional Programming - Thomas VanDrunen `

\* Lectures exist on the authorʼs homepage, this book is used for a one semester university course with additional elective chapters in Graph Theory, Complexity Theory, Automata, ect.

\* List of [64]Common Errors in Undergrad Math like undistributed cancellations, sign errors, confusion about notation ect. if you missed it from the prelims section of this guide

\* The Art of Computer Programming Vol 1: Fundamental Algorithms by Donald E. Knuth also offers an excellent but terse introduction to Discrete Math

Great Theoretical Ideas in Computer Science

This is a crash course in multiple topics such as Probability, Linear Algebra, Modular Arithmetic, Polynomials, Cryptography and Complexity Theory. 15-251 assumes students have completed An Infinite Descent into Pure Mathematics though it is self contained. "This course teaches the mathematical underpinnings of computation and explores some of the central results and questions regarding the nature of computation."

Computer Systems

This covers computer architecture from a programmerʼs perspective, such as how to write cache friendly code, and other optimizations for the x86-64 arch. You learn how to manually write loops in assembly and see how recursion works at a lower abstraction. You learn machine code instructions, how compilers work, return oriented programming (ROP) to bypass stack protections, the memory hierarchy, and networks. You could read K&Rʼs The C Programming Language for a brief intro, though this course will explain C as you go anyway and fully covers pointers at the assembly language level making it self contained.

Designing Systems

Knuth constructed a futuristic architecture with 256 registers for his Art of Computer Programming series to make hacking around with experimental systems programming easier. If you ever find yourself implementing instructions for something (webAssembly devs) you should read through [65]MMIX documentation, for gems like MOR/MXOR.

`\* [66](Book) MMIXware: a RISC computer for the third millennium - Donald E Knuth `

\* An intro to MMIX [67]here with some of itʼs interesting design (such as implementing proper integer arithmetic) and [68]simulators

\* Knuth uses literate programming to go through all design decisions, describing every line of code in detail as itʼs how he learned to write symbolic assemblers in the 1950s by reading other peopleʼs [69]programs while on vacation

\* Combine with some [70]course notes on open research issues for operating systems or read about the architecture and critiques of [71]Hurd

\* A [72]series of papers in Computer Architecture curated by Princeton U (use [73]sci-hub to access them)

Compilers

Search libgen.io or sci-hub for additional [74]papers or [75]books on parsers/compilers.

Database Systems

Interesting [76]post on the future of database systems by Andy Pavlo.

Practical Data Science

`\* [77](Full Course) 15-388 Practical Data Science `

\* Provides a practical introduction to the ʼfull stackʼ of data science analysis: data collection and processing, data visualization and presentation, statistical model building using machine learning, and big data techniques for scaling these methods.

\* Jupyter notebook is a [78]literate programming paradigm tool, you can freely write using LaTeX or markdown and run the code cells in any order to not interrupt the flow of documentation.

\* This course teaches the Python libraries, but you can do the assignments in [79]Julia if you want, or experiment in [80]OCaml with static typing.

A classic introductory computer science book on thinking about the big picture of programs with abstraction: finding general patterns from specific problems and building programs based on these patterns. An applied example of this is the package manager [81]GNU Guix and distro GuixSD, which is a GNU implementation of the NixOS [82]functional software deployment model. Package builds, including entire system builds, are declared in one text file. The resulting software deployment is functional: build inputs go in such as compilers, customizations, environments ect, and a reproducible, immutable build comes out with a hashed identifier meaning you can do roll backs to previous successful builds. A recent GUixSD feature [83]gexp (g-expressions) is a good example what can be achieved through syntactic extensions of the Scheme language.

You are more likely to benefit from this book after having some programming experience, but no matter what [84]level of programmer you are youʼll still benefit from SICP.

Natural Language Processing

Read [85]these reasons for studying programming languages.

Isolating Software Failure, Proving Safety and Testing

How to verify software, and strategies of programming that minimize catastrophe during failure. The [86]Little Prover is a good introduction in determining facts about computer programs. Prof Rob Simmons can be [87]hired as a tutor to teach you Coq, the curriculum follows the Software Foundations book series below.

`\* [88](Lecture Notes) 15-316 Software Foundations of Security and Privacy `

\* Covers side channel attacks, provable privacy, web security, sandboxing, assignments are extending an http network server (in C), and spotting bugs.

\* Read about tests, strategies to safe program design (in OCaml), and proving safety from the 2017 version: git clone [89]https://github.com/jeanqasaur/cmu-15316-spring17.git

\* [90](Book) Verified Functional Algorithms

\* Some recorded lectures by Andrew Appel [91]here

\* Part 3 of the [92]Deep Specifications interactive book series by Andrew Appel, learn by doing

\* Assumes you have read [93]these chapters of Software Foundations Part I: Preface, Basics, Induction, Lists, Poly, Tactics, Logic, IndProp, Maps, (ProofObjects), (IndPrinciples)

\* A good introduction to Dependent Types by Dan Licata is [94]here or The Little Typer [95]book.

Physical Systems Software Security

`\* [96](Full Course) 15-424 Foundations of Cyber-Physical Systems `

\* Course (with recorded lectures) if youʼre interested in programming drones/space shuttles/robots/cars and other things that cannot fail from avoidable errors.

\* Self contained, will teach you differential eq but assumes you already have some calculus background.

Algorithms

Unlike a traditional introduction to algorithms and data structures, this puts an emphasis on thinking about how algorithms can do multiple things at once instead of one at a time and ways to design to exploit these properties. It uses a language based analysis model to estimate complexity for abstractions such as garbage collection.

Advanced Algorithms (Purely Functional Data Structures)

The typical text for a senior undergrad or graduate course on algorithm analysis is The Design and Analysis of Algorithms by Kozen (1992). This is an excellent book to study with difficult problems well presented and clearly analyzed by Kozen. The Okasaki book (1999), Purely Functional Data Structures gives examples in Standard ML and Haskell.

Complexity Theory

Expands on the lectures in 15-251.

Graduate Complexity Theory

You may want to [97]try solving some of the problems in this domain.

Useful Math for Theoretical CS

These scribed lecture [98]notes give you a diverse background in math useful for theoretical CS, from the excellent book [99]The Nature of Computation such as these [100]slides from A Theoristʼs ToolKit which describe how to find research, how to write math in LaTeX, how to give a talk, resources on math.overflow ect.

Introduction to Quantum Computing

This is a graduate introduction to quantum computation/information theory, from the perspective of theoretical computer science.

`\* [101](Full Course) 15-859BB: Quantum Computation `

\* Recorded lectures on [102]YouTube.

\* The prereqs are an undergrad background in complexity theory (15-251 or 15-455), linear algebra, and discrete [103]probability.

\* "90% of the understanding of the quantum circuit model is achieved by reviewing three purely ʼclassicalʼ topics: classical Boolean circuits; reversible classical circuits; and randomized computation"

\* A [104]series of curated papers on Quantum Computing

Jobs

This [105]service matches your skills to people who want to pay you. Jane Street Capital is a finance tech company that hires functional programmers worldwide, you may want to [106]apply there. There are numerous opportunities to apprentice as a [107]security researcher or get paid to prove business logic programs like ʼsmart contractsʼ. Check your local university employment listings as well, often students do not take these jobs as they are off chasing that startup stock options payday or doing industry internships, plenty of opportunities to work with post-doc researchers writing algorithms or analyzing data. For web programming there exists free programming [108]notes for software like NodeJS, which you can write software for in OCaml by compiling it into JS with [109]bucklescript or compile to web assembly.

Graduate Research in Type Theory

Read these [110]slides from A Theoristʼs ToolKit on how to find research, how to write math in LaTeX, how to give a talk, where to ask on stackexchange ect.

Basic Proof Theory

Intro to Category Theory

Type Theory Foundations

Higher Dimensional Type Theory

Start with this [111]talk A Functional Programmerʼs Guide to Homotopy Type Theory with intro to Cubical Type Theory

Further Research

Graduate Research in Machine Learning/AI

Read these [112]slides from A Theoristʼs ToolKit on how to find research, how to write math in LaTeX, how to give a talk, where to ask on stackexchange ect.

Graduate Introduction to General AI

These notes [113]here cover more topics, such as Computer Vision/NLP/AGI and follows AI: A Modern Approach book by Norvig. The 15-780 course is specific to some topics in AI providing them 2-3 lectures each.

`\* [114](Full Course) 15-780 Graduate Introduction to AI `

\* Recorded lectures [115]here and [116]here

\* No formal pre-requisites except grad level standing so familiar with some undergrad calculus and linear algebra/probability

\* MITʼs Artificial General Intelligence [117]free class explores possible research paths for creating AGI

\* Sussman had a great 2017 grad [118]course reviewing older paper with unsolved problems or deep ideas, like his own Art of the Propagator paper and [119]implementation

\* AI [120]readings to explain intelligence from a computational point of view

Math Background for ML

A series of [121]recorded lectures and recommended [122]texts for a crash course in the Math background for ML introductory courses

Statistics Theory

`\* [123](Full Course) 36-705 Intermediate Statistics `

\* The author of All of Statistics Larry Wasserman covers the fundamentals of theoretical statistics in these (badly) recorded lectures

\* The probability prereqs can be found [124]here or from any undergrad text on probability

\* Additional lecture [125]notes on the intersection of stats theory and ML

Graduate Introduction to ML

Advanced Introduction to ML

`\* [126](Full Course) 10-715 Advanced Introduction to ML `

\* Most recent version [127]here uses mostly the same recorded lectures.

\* Fast paced curriculum intended to prepare PhD students in the ML grad program to write research papers

\* Some [128]slides on the practical techniques needed for working with large datasets

\* Linear Algebra review crash course w/recorded lectures [129]here relevant to optimization/ML

\* Real Analysis review [130]here

\* Probability lecture [131]notes

\* [132](Full Course) 10-701 Introduction to ML

\* Intended for PhD students outside the ML program, more theory and rigor than 10-601

\* Assumes you have this Math Background for ML math background

\* Some recorded recitations are [133]here

\* Some [134]slides on the practical techniques needed for working with large datasets

Convex Optimization

Deep Learning

If youʼre interested in parallel GPU programming for training see these [135]lectures and [136]notes.

`\* [137](Full Course) 11-785 Introduction to Deep Learning `

\* Recorded lectures [138]here to learn to build and tune deep learning models

\* If the playlist is deleted, which is frequently, (use youtube-dl to archive) search YouTube for "CMU 11-785"

\* There are also applied courses and practical challenges/competitions on kaggle.com

\* Some [139]slides on techniques for making deep learning robust to adversarial manipulation

Algorithms for Big Data

Further Research

Graduate Research Elective: Cryptography

Read these [140]slides from A Theoristʼs ToolKit on how to find research, how to write math in LaTeX, how to give a talk, where to ask on stackexchange ect.

Graduate Cryptography Intro

This course covers post-quantum crypto, elliptic curve crypto, and is taught by premiere researcher Tanja Lange (TU/e)

Applied Cryptography

`\* Draft [141]book A Graduate Course in Applied Cryptography - Dan Boneh and Victor Shoup `

\* Lecture [142]notes from 18-733 Applied Cryptography

\* Read all Daniel J. Bernsteinʼs (and Peter Gutmannʼs) posts on the IETF Crypto Forum Research Group [Cfrg]archive, itʼs a [143]master class in modern [144]cryptanalysis and he rips apart bad standards/protocol designs.

\* Read The Art of Computer Programming - Seminumerical Algorithms by Knuth (Vol 2) chapter on Random Numbers. These tests are still used in MIT grad courses. Try them on every library you can find that supposedly generates pseudorandom numbers

\* [145]Read about the proof of the Wireguard protocol, a VPN that uses AEAD_CHACHA20_POLY1305

Future Research

`\* Follow whatever the PhD students of djb and Tanja Lange are [146]working on `

\* [147]Watch the lectures from the 2017 Post-Quantum Crypto Summer School

\* Read the journal of [148]Crypto Engineering (use SciHub proxy)

\* Read a [149]book on Random Graphs there is a [150]connection between Graph Theory and Cryptography

\* Try the [151]Cryptopals challenges

\* Read some cryptocurrency papers, such as Stellarʼs Consensus algorithm (soon to be used by mobilecoin.com), [152]Fail-Safe Network or the [153]protocol specification for Zcash.

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73. https://en.wikipedia.org/wiki/Sci-Hub

74. https://cc-conference.github.io/18/accepted-papers/

75. https://en.wikipedia.org/wiki/Compilers:

76. http://www.cs.cmu.edu/~./pavlo/blog/2015/09/the-next-50-years-of-databases.html

77. http://www.datasciencecourse.org/lectures/

78. http://www.literateprogramming.com/index.html

79. http://ucidatascienceinitiative.github.io/IntroToJulia/

80. http://ocaml.xyz/

81. http://dustycloud.org/misc/talks/guix/chicagolug_2015/guix_talk.html

82. https://arxiv.org/pdf/1305.4584.pdf

83. https://arxiv.org/pdf/1709.00833.pdf

84. https://twitter.com/id_aa_carmack/status/350028210551013376?lang=en

85. http://www.cs.cmu.edu/~rwh/courses/ppl/phil.html

86. https://mitpress.mit.edu/books/little-prover

87. https://calculem.us/classes/

88. https://15316-cmu.github.io/index.html

89. https://github.com/jeanqasaur/cmu-15316-spring17.git

90. http://www.cs.princeton.edu/~appel/vfa/

91. https://deepspec.org/event/dsss17/lecture_appel.html

92. https://deepspec.org/page/SF/

93. https://softwarefoundations.cis.upenn.edu/current/index.html

94. https://youtu.be/LXvP1A97oAM

95. https://mitpress.mit.edu/books/little-typer

96. http://symbolaris.com/course/fcps16.html

97. https://news.ycombinator.com/item?id=6346697

98. http://www.cs.cmu.edu/~odonnell/toolkit13/

99. http://www.nature-of-computation.org/

100. http://www.cs.cmu.edu/~15751/2016-lecture1.pdf

101. http://www.cs.cmu.edu/~odonnell/quantum18/

102. https://www.youtube.com/playlist?list=PLm3J0oaFux3YL5qLskC6xQ24JpMwOAeJz

103. http://www.cs.cmu.edu/~odonnell/papers/probability-and-computing-lecture-notes.pdf

104. https://www.morganclaypool.com/toc/qmc/1/1

105. https://triplebyte.com/

106. https://blogs.janestreet.com/interviewing-at-jane-street/

107. https://www.nccgroup.trust/us/about-us/careers/current-vacancies/security-consultant/

108. https://books.goalkicker.com/

109. https://bucklescript.github.io/

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111. https://youtu.be/caSOTjr1z18

112. http://www.cs.cmu.edu/~15751/2016-lecture1.pdf

113. http://www.cs.cmu.edu/~./15381/#schedule

114. http://www.cs.cmu.edu/afs/cs/Web/People/15780/#schedule

115. https://www.youtube.com/playlist?list=PLpIxOj-HnDsPfw9slkk0BfwuiNEYVnsd

116. https://www.youtube.com/playlist?list=PL_Ig1a5kxu55zgIn3Tb5Pc5C62Yo2sYHf

117. https://agi.mit.edu/

118. https://ai6034.mit.edu/wiki/index.php?title=6.S966:_A_Graduate_Section_for_6.034#Prospectus

119. http://groups.csail.mit.edu/mac/users/gjs/propagators/

120. https://courses.csail.mit.edu/6.803/schedule.html

121. https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA

122. https://canvas.cmu.edu/courses/603/assignments/syllabus

123. http://www.stat.cmu.edu/~larry/=stat705/

124. https://www.youtube.com/playlist?list=PL_Ig1a5kxu57qPZnHm-ie-D7vs9g7U-Cl

125. http://www.stat.cmu.edu/~larry/=sml/

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131. http://www.stat.cmu.edu/~siva/700/main.html

132. http://www.cs.cmu.edu/~epxing/Class/10701/lecture.html

133. https://www.youtube.com/playlist?list=PL_Ig1a5kxu54nrfuy4V0V1eHJk3C4dV3X

134. http://curtis.ml.cmu.edu/w/courses/index.php/Syllabus_for_Machine_Learning_with_Large_Datasets_10-605_in_Fall_2017

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139. http://www.archive.ece.cmu.edu/~ece739/schedule.html

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141. https://crypto.stanford.edu/~dabo/cryptobook/

142. https://www.ece.cmu.edu/~ece733/schedule.html

143. https://mailarchive.ietf.org/arch/msg/cfrg/E-q_6ABdKfPU5XG2N6IVAs_sNhA/?qid=435ba2be2c3db3444e338cc0259ee124

144. https://mailarchive.ietf.org/arch/msg/cfrg/oOY_QNA6QMsTJHLLbUjch6-GLsc/?qid=435ba2be2c3db3444e338cc0259ee124

145. https://www.wireguard.com/formal-verification/

146. https://hyperelliptic.org/tanja/students.html

147. https://2017.pqcrypto.org/school/schedule.html

148. https://link.springer.com/journal/13389

149. http://www.math.cmu.edu/~af1p/BOOK.pdf

150. http://math.nsc.ru/conference/g2/g2c2/TokarevaN.pdf

151. https://cryptopals.com/

152. https://medium.com/@homakov/introducing-failsafe-network-ea47ab476fe6

153. https://github.com/zcash/zips/blob/master/protocol/protocol.pdf

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