University Courses
Contents
Systems
ECE 459
Learn techniques for profiling, rearchitecting, and implementing software systems that can handle industrial-sized inputs, and to design and build critical software infrastructure. Learn performance optimization through parallelization, multithreading, async I/O, vectorization and GPU programming, and distributed computing.
This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in both high-performance transaction processing systems (OLTP) and large-scale analytical systems (OLAP). The class will stress both efficiency and correctness of the implementation of these ideas. All class projects will be in the context of a real in-memory, multi-core database system. The course is appropriate for graduate students in software systems and for advanced undergraduates with strong systems programming skills.
CSEP 552
CSEP552 is a graduate course on distributed systems. Distributed systems have become central to many aspects of how computers are used, from web applications to e-commerce to content distribution. This course will cover abstractions and implementation techniques for the construction of distributed systems, including client server computing, the web, cloud computing, peer-to-peer systems, and distributed storage systems. Topics will include remote procedure call, maintaining consistency of distributed state, fault tolerance, high availability, and other topics. As we believe the best way to learn the material is to build it, there will be a series of hands-on programming projects.
15-440
Programming Languages / Compilers
This is a course on the study, design, and implementation of programming languages.
Taught by Gerald Sussman of SICP fame, this class deals with concepts and techniques for the design an implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications.
Taught by Gerald Sussman of SICP fame, this class deals with concepts and techniques for the design an implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications.
Algorithms
The link to labs and projects is included in the website. 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.
MIT
Advanced Algorithms Advanced course in algorithms by Dr. David Karger covering topics such as amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms.
CS Theory
Software Foundations University of Pennsylvania An introduction to formal verification of software using the Coq proof assistant. Topics include basic concepts of logic, computer-assisted theorem proving, functional programming, operational semantics, Hoare logic, and static type systems.
Discrete Structures Univ of Illinois Urbana-Champaign This course is an introduction to the theoretical side of computer science. In it, you will learn how to construct proofs, read and write literate formal mathematics, get a quick introduction to key theory topics and become familiar with a range of standard mathematics concepts commonly used in computer science.
Algorithms & Models of Computation (Fall 2014) University of Illinois Urbana-Champaign CS 498 section 374 (unofficially "CS 374") covers fundamental tools and techniques from theoretical computer science, including design and analysis of algorithms, formal languages and automata, computability, and complexity. Specific topics include regular and context-free languages, finite-state automata, recursive algorithms (including divide and conquer, backtracking, dynamic programming, and greedy algorithms), fundamental graph algorithms (including depth- and breadth-first search, topological sorting, minimum spanning trees, and shortest paths), undecidability, and NP-completeness. The course also has a strong focus on clear technical communication.
Introduction to Theory of Computing Cornell University This undergraduate course provides a broad introduction to the mathematical foundations of computer science. We will examine basic computational models, especially Turing machines. The goal is to understand what problems can or cannot be solved in these models.
Introduction to CS
Structure & Interpretation of Computer Programs [Racket] UC Berkeley <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - A self-paced version of the CS61 Course but in Racket / Scheme. 61AS is a great introductory course that will ease you into all the amazing concepts that future CS courses will cover, so remember to keep an open mind, have fun, and always respect the data abstraction - Lecture Videos - Assignments and Notes
Structure and Interpretation of Computer Programs * MIT <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - Teaches big-picture computing concepts using the Scheme programming language. Students will implement programs in a variety of different programming paradigms (functional, object-oriented, logical). Heavy emphasis on function composition, code-as-data, control abstraction with continuations, and syntactic abstraction through macros. An excellent course if you are looking to build a mental framework on which to hang your programming knowledge. - Lectures - Textbook (epub, pdf) - IDE
Structure and Interpretation of Computer Programs * MIT
Teaches big-picture computing concepts using the Scheme programming language. Students will implement programs in a variety of different programming paradigms (functional, object-oriented, logical). Heavy emphasis on function composition, code-as-data, control abstraction with continuations, and syntactic abstraction through macros. An excellent course if you are looking to build a mental framework on which to hang your programming knowledge.
Machine Learning
This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. This course is organised by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.
Data Science Harvard University <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
Deep Learning for Natural Language Processing Stanford University <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
This course is taught through the Data Science Center at NYU. Originally introduced by Yann Lecun, it is now led by Zaid Harchaoui, 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.
CS20si
This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
Fast.ai Introduction to Machine Learning for Coders
There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge.
Practical_RL
CS 109
Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.
Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. (slightly better)
Security
CSCI 4968
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.
CSCI 4976
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 solely by students, primarily using the book by Michael Sikorski and Andrew Honig, to teach skills in reverse engineering, malicious behaviour, malware, and anti-analysis techniques.
Artificial Intelligence
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
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.
The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
This course is an introduction, by Prof. Marvin Minsky, 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.
Computer Graphics
CS 378
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
Misc
Open Source Software Construction UC Riverside <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - This is a course on how to be a hacker. Your first four homework assignments walk you through the process of building your own unix shell. You'll be developing it as an open source project, and you will collaborate with each other at various points. - Github Page - Assignments
GIS & Spatial Analysis UC Santa Barbara <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - Taught by James Frew, Ben Best, and Lisa Wedding - Focuses on specific computational languages (e.g., Python, R, shell) and tools (e.g., GDAL/OGR, InVEST, MGET, ModelBuilder) applied to the spatial analysis of environmental problems - GitHub (includes lecture materials and labs)
Software Engineering University of Hawaii <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> - Taught by Philip Johnson - Introduction to software engineering using the "Athletic Software Engineering" pedagogy - Readings - Experiences - Assessments
Intro to Ruby on Rails University of Texas <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - An introductory course in Ruby on Rails open sourced by University of Texas' CS Adjunct Professor, Richard Schneeman. - Lectures - Assignments - Videos
Intro to Game Developement Harvard University <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - This course picks up where Harvard College’s CS50 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 LÖVE 2D, 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. - Assignments - Lecture Videos
This course provides students with exposure to the design, creation and production of Open Source Software projects. Students will be introduced to the historic intersections of technology and intellectual property rights and will become familiar with Open Source development processes, tools and practices.
Android App Development, Spring 2016
Android App Development, Spring 2016 Stanford University <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - Course Description: This course provides an introduction to developing applications for the Android mobile platform. - Prerequisite: CS 106B or equivalent. Java experience highly recommended. OOP highly recommmended. - Devices: Access to an Android phone and/or tablet recommended but not required. - 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 here to download them.
GIS & Spatial Analysis
GIS & Spatial Analysis UC Santa Barbara <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - Taught by James Frew, Ben Best, and Lisa Wedding - Focuses on specific computational languages (e.g., Python, R, shell) and tools (e.g., GDAL/OGR, InVEST, MGET, ModelBuilder) applied to the spatial analysis of environmental problems - GitHub (includes lecture materials and labs)
Software Engineering
Software Engineering University of Hawaii <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> - Taught by Philip Johnson - Introduction to software engineering using the "Athletic Software Engineering" pedagogy - Readings - Experiences - Assessments
Intro to Ruby on Rails
Intro to Ruby on Rails University of Texas <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> - An introductory course in Ruby on Rails open sourced by University of Texas' CS Adjunct Professor, Richard Schneeman. - Lectures - Assignments - Videos
Awesome CS Courses
Courses
Compilers
University of Utah Assignments Lecture Notes Readings If you're a fan of Prof Matt's writing on his fantastic blog 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 cheating the page has tons of interesting links on programming languages, parsing and compilers.
CPython internals: A ten-hour codewalk through the Python interpreter source code
Programming Languages
Compiler Construction
University of Washington Lecture Videos Assignments Teaches understanding of how a modern compiler is structured and the major algorithms that are used to translate code from high-level to machine language. The best way to do this is to actually build a working compiler, so there will be a significant project to implement one that translates programs written in a core subset of Java into executable x86 assembly language. The compilers themselves will use scanner and parser generator tools and the default implementation language is Java.
Discrete Mathematics and Functional Programming
Adventures in Advanced Symbolic Programming
MIT Lecture Notes Assignments Readings Taught by Gerald Sussman of SICP fame, this class deals with concepts and techniques for the design an implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications.
Advanced Functional Programming
University of Cambridge Lecture Notes Assignments Readings This module aims to teach students how to use the features of modern typed functional programming languages (e.g. OCaml, Haskell) to design and implement libraries and DSLs. It aims to demonstrate how such techniques can improve both correctness and efficiency. Students wishing to take the module should have some experience of a typed functional programming language and an understanding of type inference. This particular session was taught by a prominent OCaml programmer, open Source contributor & author of real world OCaml - Dr Anil Madhavapeddy.