Table of Contents

,

Awesome Courses

Awesome

(https://github.com/sindresorhus/awesome)

출처: https://github.com/prakhar1989/awesome-courses/blob/master/README.md

Introduction

There is a lot of ~~hidden~~ treasure lying within university pages scattered across the internet. This list is an attempt to bring to light those awesome courses which make their high-quality material i.e. assignments, lectures, notes, readings & examinations available online for free.

Table of Contents

Legend

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- - Lecture Videos

Courses

Systems

Programming Languages / Compilers

Algorithms

CS Theory

Introduction to CS

Machine Learning

Security

- CIS 4930 / CIS 5930 Offensive Computer Security Florida State University

  1. Course taught by W. Owen Redwood and Xiuwen Liu. 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.
  2. Lectures and Videos
  3. Assignments

- CS 155 Computer and Network Security Stanford

  1. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system holes, application security (web, email, databases), viruses, social engineering attacks, privacy, and digital rights management. Course projects focus on building reliable code. Recommended: Basic Unix. Primarily intended for seniors and first-year graduate students.

- CS 161 Computer Security UC Berkeley

  1. Introduction to computer security. Cryptography, including encryption, authentication, hash functions, cryptographic protocols, and applications. Operating system security, access control. Network security, firewalls, viruses, and worms. Software security, defensive programming, and language-based security. Case studies from real-world systems.

- CS 259 Security Modeling and Analysis Stanford

  1. The course will cover a variety of contemporary network protocols and other systems with security properties. The course goal is to give students hands-on experience in using automated tools and related techniques to analyze and evaluate security mechanisms. To understand security properties and requirements, we will look at several network protocols and their properties, including secrecy, authentication, key establishment, and fairness. In parallel, the course will look at several models and tools used in security analysis and examine their advantages and limitations. In addition to fully automated finite-state model checking techniques, we will also study other approaches, such as constraint solving, process algebras, protocol logics, probabilistic model checking, game theory, and executable models based on logic programming.

- CS 261 Internet/Network Security UC Berkeley

  1. This class aims to provide a thorough grounding in network security suitable for those interested in conducting research in the area, as well as students more generally interested in either security or networking. We will also look at broader issues relating to Internet security for which networking plays a role. Topics include: denial-of-service; capabilities; network intrusion detection; worms; forensics; scanning; traffic analysis / inferring activity; architecture; protocol issues; legality and ethics; web attacks; anonymity; honeypots; botnets; spam; the underground economy; research pitfalls. The course is taught with an emphasis on seminal papers rather than bleeding-edge for a given topic.

- CS 5430 System Security Cornell University

  1. This course discusses security for computers and networked information systems. We focus on abstractions, principles, and defenses for implementing military as well as commercial-grade secure systems.
  2. Syllabus
  3. Lectures
  4. Assignments

- CSCI 4968 Modern Binary Exploitation Rensselaer Polytechnic Institute

  1. This repository contains the materials as developed and used by RPISEC to

teach Modern Binary Exploitation at Rensselaer Polytechnic Institute 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.

  1. Lectures Notes
  2. Labs
  3. Projects

- CSCI 4976 Malware Analysis Rensselaer Polytechnic Institute

  1. This repository contains the materials as developed and used by RPISEC to

teach Malware Analysis at Rensselaer Polytechnic Institute in
Fall 2015. This was a university course developed and run soley by students, primarily using the
- EECS 588 Computer & Network Security University of Michigan

  1. Taught by J. Alex Halderman who has analyzed the security of Electronic Voting Machines in the US and over seas.
  2. This intensive research seminar covers foundational work and current topics in computer systems security.
  3. Readings

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

  1. Lectures Notes
  2. Labs
  3. Projects

- 6.857 Computer and Network Security MIT

  1. 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.

Lecture Notes 

[References](http://courses.csail.mit.edu/6.857/2015/references)

- 6.858 Computer Systems Security MIT

  1. Design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.
  2. Taught by James Mickens and Nickolai Zeldovich
  3. Video Lectures and Labs
  4. Quizzes
  5. Readings
  6. Final Projects

- 18-636 Browser Security Stanford

  1. The Web continues to grow in popularity as platform for retail transactions, financial services, and rapidly evolving forms of communication. It is becoming an increasingly attractive target for attackers who wish to compromise users' systems or steal data from other sites. Browser vendors must stay ahead of these attacks by providing features that support secure web applications. This course will study vulnerabilities in existing web browsers and the applications they render, as well as new technologies that enable web applications that were never before possible. The material will be largely based on current research problems, and students will be expected to criticize and improve existing defenses. Topics of study include (but are not limited to) browser encryption, JavaScript security, plug-in security, sandboxing, web mashups, and authentication.

Artificial Intelligence

Computer Graphics

- CAP 5415 Computer Vision University of Central Florida

  1. An introductory level course covering the basic topics of computer vision, and introducing some fundamental approaches for computer vision research.
  2. Lectures and Videos
  3. Assignments

- CIS 581 Computer Vision and Computational Photography University of Pennsylvania

  1. An introductory course in computer vision and computational photography focusing on four topics: image features, image morphing, shape matching, and image search.
  2. Lectures
  3. Assignments

- CMU 462 Computer Graphics Carnegie Mellon University

  1. This course provides a comprehensive introduction to computer graphics. Focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis.
  2. Lectures and Readings
  3. Assignments and Quizes

- CS 123 Introduction to Computer Graphics Brown University

  1. This course offers an in-depth exploration of fundamental concepts in 2D and 3D computer graphics. It introduces 2D raster graphics techniques, including scan conversion, simple image processing, interaction techniques and user interface design. The bulk of the course is devoted to 3D modeling, geometric transformations, and 3D viewing and rendering.
  2. Lectures
  3. Labs
  4. Demos

- CS 378 3D Reconstruction with Computer Vision UTexas

  1. In this lab-based class, we'll dive into practical applications of 3D reconstruction, combining hardware and software to build our own 3D environments from scratch. We'll use open-source frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying state-of-the art approaches to geometric computer vision
  2. Lectures

- CS 4620 Introduction to Computer Graphics Cornell University

  1. The study of creating, manipulating, and using visual images in the computer.
  2. Assignments
  3. Exams

- CS 4670 Introduction to Computer Vision Cornell University

  1. This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
  2. Assignments
  3. Lectures

- CS 6670 Computer Vision Cornell University

  1. Introduction to computer vision. Topics include edge detection, image segmentation, stereopsis, motion and optical flow, image mosaics, 3D shape reconstruction, and object recognition. Students are required to implement several of the algorithms covered in the course and complete a final project.
  2. Syllabus
  3. Lectures
  4. Assignments

- CSCI-GA.2270-001 Graduate Computer Graphics New York University

  1. Step-by-step study computer graphics, with reading and homework at each lecture (Fall2015)
  2. Lectures

Misc