Computer Science Resources

Video lectures on various topics:

  • MIT 6.875 (Cryptography)
  • UC Berkeley CS188 Intro to AI
  • Erlang Master class
  • Machine Learning 10-701/15-781

    Homepages of Computer Science academic courses:

  • Stanford CS144: Introduction to Computer Networking
  • MIT 6.S191: Introduction to Deep Learning
  • CS 525 Advanced Distributed Systems
  • 6.046J Design and Analysis of Algorithms
  • 6.006 Introduction to Algorithms
  • 11-785 Introduction to Deep Learning
  • MIT OCW 6.035 Computer Language Engineering
  • CS 421/521: Compilers and Interpreters
  • CS 6120: Advanced Compilers: The Self-Guided Online Course
  • Foundations of Probabilistic Programming
  • Advanced Functional Programming
  • CS 4110: Programming Languages and Logics
  • 6.851 Advanced Data Structures
  • 6.004 Computation Structures
  • 6.080 Great Ideas in Theoretical Computer Science
  • 6.006 Introduction to Algorithms
  • 6.893 Philosophy and Theoretical Computer Science
  • Machine Learning
  • An Introduction to Computer Networks
  • cs4414: Operating Systems

    Computer Science pedagogical:

  • Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory https://arxiv.org/abs/2310.20360
  • Information Theory and Statistical Physics - Lecture Noteshttps://arxiv.org/abs/1006.1565
  • Information Theory: A Tutorial Introduction https://arxiv.org/abs/1802.05968
  • A Brief Introduction to Machine Learning for Engineers https://arxiv.org/abs/1709.02840
  • Pen and Paper Exercises in Machine Learning https://arxiv.org/abs/2206.13446
  • Formal Algorithms for Transformers https://arxiv.org/abs/2207.09238
  • A Cookbook of Self-Supervised Learning https://arxiv.org/abs/2304.12210
  • Information Theory: A Tutorial Introduction https://arxiv.org/abs/1802.05968
  • Tutorial on Diffusion Models for Imaging and Vision https://arxiv.org/abs/2403.18103

    Computer Science papers (arxiv):

  • Explainable Deep Learning: A Field Guide for the Uninitiated https://arxiv.org/abs/2004.14545
  • Dreaming neural networks: forgetting spurious memories and reinforcing pure ones https://arxiv.org/abs/1810.12217
  • Learning second order coupled differential equations that are subject to non-conservative forces https://arxiv.org/abs/2010.11270
  • Memristor - The fictional circuit element https://arxiv.org/abs/1808.05982
  • Thermodynamics of stochastic Turing machines https://arxiv.org/abs/1506.00894
  • How to Run Algorithmic Information Theory on a Computer https://arxiv.org/abs/chao-dyn/9509014v2
  • Let a Thousand Flowers Bloom: An Algebraic Representation for Edge Graphs https://arxiv.org/abs/2403.02273
  • Understanding Biology in the Age of Artificial Intelligence https://arxiv.org/abs/2403.04106
  • Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution https://arxiv.org/abs/2210.08340
  • Perfect Zero-Knowledge PCPs for #P https://arxiv.org/abs/2403.11941
  • Deep Probabilistic Programming https://arxiv.org/abs/1701.03757v2
  • Move Evaluation in Go Using Deep Convolutional Neural Networks http://arxiv.org/abs/1412.6564
  • Teaching Deep Convolutional Neural Networks to Play Go https://arxiv.org/abs/1412.3409
  • A Probabilistic Theory of Deep Learning http://arxiv.org/abs/1504.00641
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing http://arxiv.org/abs/1506.07285
  • A New Information Complexity Measure for Multi-pass Streaming with Applications https://arxiv.org/abs/2403.20283v1
  • Machine Culture https://arxiv.org/abs/2311.11388
  • Attention Is All You Need https://arxiv.org/abs/1706.03762
  • Eight Transaction Papers by Jim Gray https://arxiv.org/abs/2310.04601
  • What's the Magic Word? A Control Theory of LLM Prompting https://arxiv.org/abs/2310.04444

    Other papers / links:

  • The Original 'Lambda Papers' by Guy Steele and Gerald Sussman
  • Category Theory for Programmers