awesome-courses

Operating Systems course by the Chair of EECS, UC Berkeley

Spring 2015 lectures

Spring 2015 lectures

Taught by one of the stalwarts of this field, Prof Ken Birman, this course has a fantastic set of slides that one can go through. The Prof's is also a gem and recommended as a must read in Google's tutorial on

An introduction to operating systems including topics in system structures, process management, storage management, files, distributed systems, and case studies

For the processor, memory, and disks, we discuss how the operating system allocates each resource and explore the design and implementation of related abstractions. We also establish techniques for testing and improving system performance and introduce the idea of hardware virtualization. Programming assignments provide hands-on experience with implementing core operating system components in a realistic development environment. Course by

Just do

Note: These are student recorded cam videos of the 2011 course. The videos explain a lot of concepts required for the labs and assignments

of a previous session are available to watch

Very comprehensive material on Computer Architecture - definitely more than just "introduction". Online material is very user-friendly, even the recitation videos available online. This is the Spring'15 version by Prof

7 HWs with answer set as well

Modelled after the influential paper on , this course teaches how to build a compiler in OCaml

Uses the programming language & book to understand the fundamentals of programming languages

semester also available, with more exercises

Lecture Notes - Readings

Course by Prof. Krishnamurthi (author of ) and numerous other on programming languages. Uses a custom designed programming language to teach the concepts. There was an hosted in 2012, which includes all lecture videos for you to enjoy

3 Assignments: , ,

Course that uses OCaml to teach functional programming and programming language design

If you're a fan of Prof Matt's writing on his you ought to give this a shot. The course covers the design and implementation of compilers, and it explores related topics such as interpreters, virtual machines and runtime systems. Aside from the Prof's witty take on the page has tons of interesting links on programming languages, parsing and compilers

: Extensive programming assignments, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or other "functional" language

Lecture of Spring 2016. This website contains full matrials including video links, labs, homeworks, projects. Very good for self-learner. Also a good start for Java. And it includes some other useful resources for Java Documentation, Data Structure Resources, Git/GitHub and Java Development Resources

The link to labs and projects is included in the website

( )

( )

Prof Steven Skiena's no stranger to any student when it comes to algorithms. His seminal has been touted by many to be best for . In addition, he's also well-known for tutoring students in competitive . If you're looking to brush up your knowledge on Algorithms, you can't go wrong with this course

Taught by in 2010, this course is an undergraduate introduction to algorithm design and analysis. It features traditional topics, such as Big Oh notation, as well as an importance on implementing specific algorithms. Also featured are sorting (in linear time), graph algorithms, depth-first search, string matching, dynamic programming, NP-completeness, approximation, and randomization

This is the graduate level complement to the ECS 122A undergraduate algorithms course by in 2011. It assumes an undergrad course has already been taken in algorithms, and, while going over some undergraduate algorithms topics, focuses more on increasingly complex and advanced algorithms

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography. This course assumes that students know how to analyze simple algorithms and data structures from having taken . It introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms

This is an advanced DS course, you must be done with the course before attempting this one

Contains videos from sp2012 version, but there isn't much difference

contains the calendar as well

on to access the

The required algorithms class that go in depth into all basic algorithms and the proofs behind them. This is one of the heavier algorithms curriculums on this page. Taught by Avrim Blum and who has a Turing Award due to his contributions to algorithms. Course link includes a very comprehensive set of reference notes by Avrim Blum

( )

Written by the professor. Includes Instructor's Guide

(based on Scratch by MIT)

The course can also be taken from

The course's OpenCourseware resides

[Lectures] ( )

[Assignments] ( )

and

An intro course in the spirit of SICP designed by (one of the lead designers of Racket and author of HtDP). Mostly Racket and C, and a bit of Java, with explanations on how high level functional programming concepts relate to the design of OOP programs. Do this one before SICP if SICP is a bit too much

CS 2110 is an intermediate-level programming course and an introduction to computer science. Topics include program design and development, debugging and testing, object-oriented programming, proofs of correctness, complexity analysis, recursion, commonly used data structures, graph algorithms, and abstract data types. Java is the principal programming language. The course syllabus can easily be extracted by looking at the link to

( , )

Assignments

available on Github

Course taught by introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods

( )

The course concentrates on recognizing and solving convex optimization problems that arise in applications. Topics addressed include the following. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance. - -

This increasingly popular course is taught through the Data Science Center at NYU. Originally introduced by , it is now led by , although Prof. Lecun is rumored to still stop by from time to time. It covers the theory, technique, and tricks that are used to achieve very high accuracy for machine learning tasks in computer vision and natural language processing. The assignments are in Lua and hosted on Kaggle

Taught by

The course covers the basics of matrices and vectors, solving linear equations, least-squares methods, and many applications. It'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. EE103 is based on a book that and are currently writing. Students will use a new language called to do computations with matrices and vectors

Torch

The course focusses on neural networks and uses the deep learning library (implemented in Lua) for exercises and assignments. Topics include: logistic regression, back-propagation, convolutional neural networks, max-margin learning, siamese networks, recurrent neural networks, LSTMs, hand-writing with recurrent neural networks, variational autoencoders and image generation and reinforcement learning

The lectures cover all the material in which is a more approachable version of the (or ESL) book

Course taught by and . It covers a wide range of computer security topics, starting from Secure C Coding and Reverse Engineering to Penetration Testing, Exploitation and Web Application Hacking, both from the defensive and the offensive point of view

This repository contains the materials as developed and used by to teach Modern Binary Exploitation at in Spring 2015. This was a university course developed and run solely by students to teach skills in vulnerability research, reverse engineering, and binary exploitation

This repository contains the materials as developed and used by to teach Malware Analysis at in Fall 2015. This was a university course developed and run solely by students, primarily using the

Taught by who has analyzed the security of Electronic Voting Machines in the and

book by Michael Sikorski and Andrew Honig, to teach skills in reverse engineering, malicious behaviour, malware, and anti-analysis techniques

Emphasis on applied cryptography and may include: basic notion of systems security, cryptographic hash functions, symmetric cryptography (one-time pad, stream ciphers, block ciphers), cryptanalysis, secret-sharing, authentication codes, public-key cryptography (encryption, digital signatures), public-key attacks, web browser security, biometrics, electronic cash, viruses, electronic voting, Assignments include a group final project. Topics may vary year to year

Taught by and

This course is an introduction, by Prof. , to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning

The course taught by teaches game development initially in PyGame through Python, before moving on to addressing all facets of game development. Topics addressed include game physics, sprites, animation, game development methodology, sound, testing, MMORPGs and online games, and addressing mobile development in Android, HTML5, and iOS. Most to all of the development is focused on PyGame for learning principles

This course picks up where Harvard College’s leaves off, focusing on the development of 2D and 3D interactive games. Students explore the design of such childhood games as Super Mario Bros., Legend of Zelda, and Portal in a quest to understand how video games themselves are implemented. Via lectures and hands-on projects, the course explores principles of 2D and 3D graphics, animation, sound, and collision detection using frameworks like Unity and , as well as languages like Lua and C#. By class’s end, students will have programmed several of their own games and gained a thorough understanding of the basics of game design and development

Videos: Videos list can be found

here

Other materials: Some codes, handsout, homework ..... and lecture notes are not downloadable on the site due to login requirement. Please head to my Github repo to download them

Recommended:

Prerequisites: The historical prerequisite was to pass an entrance exam in class, which covered undergraduate operating systems material (similar to ). There is no longer an exam. However, if you have not already taken a decent undergrad OS class, you should talk with me before taking this class. The exam had the benefit of "paging in" the undergrad material, which may have been its primary value (since the pass rate was high)

In addition to basic first-order logic, when taught by Computer Science this course involves elements of Formal Methods and Automated Reasoning. Formal Methods is concerned with proving properties of algorithms, specifying programming tasks and synthesizing programs from proofs. We will use formal methods tools such as interactive proof assistants (see ). We will also spend two weeks on constructive type theory, the language used by the Coq and Nuprl proof assistants

Taught by , , and

(includes lecture materials and labs)

Taught by

Introduction to software engineering using the

Course taught by about the multi-disciplinary field algorithms inspired by naturally occurring phenomenon. This course provides introduces the following areas: L-systems, Cellular Automata, Emergence, Genetic Algorithms, Swarm Intelligence and Artificial Immune Systems. It's aim is to cover the fundamentals and enable readers to build up a proficiency in applying various algorithms to real-world problems

This is a graduate course in scientific computing created and taught by in 2014, which covers topics in computer science and statistics with applications from biology. The course is designed top-down, starting with a problem and then deriving a variety of solutions from scratch

and for direct