CS名校课程

四大

• MIT 6.0001 Introduction to Computer Science and Programming in Python

• MIT 6.004 Computation Structures

• MIT 6.006/6.046J Design and Analysis of Algorithms

• MIT 6.007 Signals and Systems

• MIT 6.080/6.089 Great Ideas in Theoretical Computer Science

• MIT 6.012 Introduction to Probability

• MIT 6.031 Software Construction

• MIT 6.035 Compilers

• MIT 6.033 Computer System Engineering

• MIT 6.036/6.867 Machine Learning

• MIT 6.042J Mathematics for Computer Science

• MIT 6.045J Automata, Computability, and Complexity

• MIT 6.050J Information theory and Entropy

• MIT 6.087/6.088/6.096 C and C++

• MIT 6.172 Performance Engineering of Software Systems

• MIT 6.175 Constructive Computer Architecture

• MIT 6.338J Parallel Computing and Scientific Machine Learning

• MIT 6.824 Distributed System

• MIT 6.827 Multithreaded Parallelism: Languages and Compilers

• MIT 6.828 Operating System

• MIT 6.830/6.814 Database System

• MIT 6.840J/6.841J/6.844 Theory of Computation

• MIT 6.851 Advanced Data Structures

• MIT 6.852J Distributed Algorithms

• MIT 6.854J Advanced Algorithms

• MIT 6.864 Advanced Natural Language Processing

• MIT 6.869 Advanced Computer Vision

• MIT 6.875 Cryptography

• MIT 6.S081 Operating System Engineering(after CMU 15-213)

• MIT 6.S965 TinyML and Efficient Deep Learning Computing

• MIT 6.801/6.819/6.869/6.866 Computer Vision

• MIT 6.876 Advanced Topics in Cryptography

• MIT 18.01/18.02 Calculus

• MIT 18.06 Linear Algebra

• MIT 18.330 Introduction to numerical analysis

• MIT 18.S096 Topics in Mathematics w Applications in Finance

• CMU 15-251Great Ideas in Theoretical Computer Science

• CMU 10-315/10-701 Machine Learning

• CMU 10-414/707/10-714 Deep Learning Systems

• CMU 10-417/10-617 Intermediate Deep Learning

• CMU 10-605/10-805 Machine Learning with Large Datase

• CMU 10-725 Convex Optimization

• CMU 10-708 Probabilistic Graphical Models

• CMU 11-411/11-611N Natural Language Processing

• CMU 11-485/11-785 Introduction to Deep Learning

• CMU 11-747 Neural Networks for NLP

• CMU 14-740 Fundamentals of Telecommunications and Computer Networks

• CMU 15-110 Principles of Computing

• CMU 15-150 Principles of Functional Programming

• CMU 15-151 Mathematical Foundations for Computer Science

• CMU 15-122 Principles of Imperative Computation

• CMU 15-210 Parallel and Sequential Data Structures and Algorithms

• CMU 15-213/15-513 Introduction to Computer Systems

• CMU 15-319/15-619/15-719 Cloud Computing

• CMU 15-356/15-856 Introduction to Cryptography

• CMU 15-410/15-605/15-712 Operating Systems

• CMU 15-411/15-611/15-745 Compiler Design and Optimizations

• CMU 15-418/15-714/15-618/15-745 Parallel Computer Architecture and Programming

• CMU 15-440/15-640/15-740 Distributed Systems

• CMU 15-441/15-641 Computer Networks

• CMU 15-445/15-645/15-721 Database Systems

• CMU 15-458/858: Discrete Differential Geometry

• CMU 15-451 Algorithms Design and Analysis

• CMU 15-455 Undergrad Complexity

• CMU 15-458/858 Discrete Differential Geometry

• CMU 15-462/662 Computer Graphics

• CMU 15-605 Operating System Design and Implementation

• CMU 15-640 Distributed Systems

• CMU 15-746 Storage Systems

• CMU 15-749 Engineering Distributed Systems

• CMU 15-751 CS Theory Toolkit

• CMU 15-799 Special Topics in Databases

• CMU 15-826 Data Mining

• CMU 15-859BB Quantum Computation and Information

• CMU 15-884 Machine Learning Systems

• CMU 16-385 Computer Vision

• CMU 18-447 Introduction to Computer Architecture

• CMU 21-228 Discrete Mathematics

• CMU DLsys & MLsys

• UCB CS 9C/CS9F C and C++

• UCB CS 10 The Beauty and Joy of Computing

• UCB CS 61A PL thoery

• UCB CS 61B Data structure, Algorithm

• UCB CS 61C Computer System

• UCB CS 70 Discret Mathematics and Probability Theory

• UCB CS 126 Probability theory

• UCB CS 152/252 Computer Architecture and Engineering

• UCB CS 162 Operating System

• UCB CS 164 Programming Languages and Compilers

• UCB CS 170 Efficient Algorithms and Intractable Problems

• UCB CS 171 Cryptography

• UCB CS 172 Computability and Complexity

• UCB CS 174 Combinatorics and Discrete Probability

• UCB CS 182 Designing, Visualizing and Understanding Deep Neural Networks

• UCB CS 184/284a Computer Graphics and Imaging

• UCB CS 188/189/289 A Introduction to Machine Learning

• UCB CS 265 Compiler Optimization and Code Generation

• UCB CS 267 Parallel Computing

• UCB CS 270. Combinatorial Algorithms and Data Structures

• UCB CS 274 Computational Geometry

• UCB CS 280 Computer Vision

• UCB CS 294-112/285 Deep Reinforcement Learning

• UCB CS 294-158 Deep Unsupervised Learning

• UCB EE16 A&B Designing Information Devices and Systems I&II

• UCB EE 120 Signal and Systems

• UCB CS 294 AI-sys

• UCB CS 294/194-238 Zero Knowledge Proofs

• Stanford CS Math19/20/21 Calculus

• Stanford CS 41 python

• Stanford CS 101 Introduction to Computing Principles

• Stanford CS 104 Linear Algebra

• Stanford CS 103 Mathematical Foundations of Computing

• Stanford CS 106B/X/L Programming Abstractions in C++

• Stanford CS 107 Computer Organization & Systems

• Stanford CS 109 Probability for Computer Scientists

• Stanford CS 110 Principles of Computer Systems

• Stanford CS 110L Rust

• Stanford CS 140 Operating System

• Stanford CS 143 Compilers

• Stanford CS 144 Introduction to Computer Networking

• Stanford CS 145/245/346 Data Management and Data Systems

• Stanford CS 149 Parallel Computing

• Stanford CS 154 Intro Automata and Complexity Theory

• Stanford CS 161 Design and Analysis of Algorithms

• Stanford EE 180 Digital System Architecture

• Stanford CS 224n Natural Language Processing with Deep Learning

• Stanford CS 224W Machine Learning with Graphs

• Stanford CS 229 Machine Learning

• Stanford CS 230 Deep Learning

• Stanford CS 231n Deep Learning for Computer Vision

• Stanford CS 246 Mining Massive Data Sets

• Stanford CS 261 A Second Course in Algorithms

• Stanford CS 294-158 Deep Unsupervised Learning

• Stanford CME 323 Distributed Algorithms and Optimization

• Stanford EE 364A Convex Optimization I

其他

• Harvard CS 50 Introduction to Computer Science

• Harvard CS 109A/109B Data Science

• RICE Comp 412 Compiler Construction for Undergraduates

• NYU DS1003 Machine Learning

• THU 高级机器学习

• THU 组合数学

• PKU 计算概论

• USTC 凸优化

• ETH Zurich Digital Design and Computer Architecture

• ETH Zurich Computer Architecture (after CMU 18-447)

• Duke Introductory C Programming Specialization

• Cambridge The Information Theory, Pattern Recognition, and Neural Networks

• UMich EECS 498-007 / 598-005 Deep Learning for Computer Vision

• UIUC CS357 Numerical Methods I

• MSU CSE 251 Programming in C