Skip to content

Latest commit

 

History

History
83 lines (80 loc) · 4.06 KB

tree.md

File metadata and controls

83 lines (80 loc) · 4.06 KB
markmap
colorFreezeLevel embedAssets maxWidth initialExpandLevel
3
true
300
1

  • ML Courses

    • General

      • How do we frame a problem and select data that will be useful for making decisions?
      • How do we select a learning algorithm, choose its parameters, and measure its performance?
      • 6.390 [6.036] Intro to Machine Learning
      • 6.C01 Modeling with Machine Learning: from Algorithms to Applications
      • 6.796 Deep Learning
      • 6.790 [6.867] Machine Learning

    • Statistics

      • How do we use noisy data to make conclusions?
      • How can we measure how sure (or unsure) we are about these conclusions?
      • 18.05 Introduction to Probability and Statistics
      • 18.650 Fundamentals of Statistics
      • 6.372 [6.401] Introduction to Statistical Data Analysis
      • 6.S951 Modern Mathematical Statistics
      • …plus more in Courses 14 and 15.

    • Inference

      • What do we believe when new information conflicts with old knowledge?
      • How can we understand random, complex processes driven by lots of variables?
      • 6.3700 [6.041] Introduction to Probability
      • 6.3800 [6.008] Introduction to Inference
      • 6.7700 [6.436] Fundamentals of Probability
      • 6.7800 [6.437] Inference and Information
      • 6.7810 [6.438] Algorithms for Inference
      • 6.7830 [6.435] Bayesian Modeling and Inference

    • Theory

      • How does our model's performance depend on how much data we have?
      • What can we prove about the performance of optimization and reinforcement learning algorithms?
      • 6.7250 [6.485] Optimization for Machine Learning
      • 6.7910 [6.860] Statistical Learning Theory and Applications
      • 6.7940 [6.231] Dynamic Programming and Reinforcement Learning
      • 6.7950 [6.246] Reinforcement Learning: Foundations and Methods
      • …plus parts of 6.7900 [6.867] and the inference classes.

    • Systems

      • How can we adapt machine learning to the software and hardware it runs on?
      • What are other ways to approach programming as a whole, and when are they effective?
      • 6.5931 [6.812] Hardware Architecture for Deep Learning
      • 6.S079 Software Systems for Data Science
      • 6.S965 TinyML and Efficient Deep Learning Computing
      • 6.S981 Introduction to Program Synthesis
      • 6.S042/6.5820 Computer Networks

    • Society

      • Who is harmed from how machine learning is used? Who benefits?
      • Who gets to decide how machine learning is used? And how should it be used?
      • 6.3950 [6.404] AI, Decision Making, and Society
      • 6.4590 [6.805] Foundations of Information Policy
      • 6.C40/24.C40 Ethics of Computing

    • Cognition

      • How can we model how humans think?
      • What can machine learning practitioners learn from neurobiology?
      • 6.4120 [6.804] Computational Cognitive Science
      • 6.S899 Brain Algorithms
      • 6.S978 Tissue vs. Silicon in Machine Learning

    • Applications

      • 6.3730 Statistics, Computation and Applications
      • 6.7930 [6.871] Machine Learning for Healthcare
      • 6.8200 [6.484] Sensorimotor Learning
      • 6.4300 Introduction to Computer Vision
      • 6.8301 [6.819] Advances in Computer Vision
      • 6.8611 [6.806] Quantitative Methods for Natural Language Processing
      • 6.8620 [6.345] Spoken Language Processing
      • 6.8711 [6.802] Computational Systems Biology: Deep Learning in the Life Sciences
      • 6.S980 Machine Learning for Inverse Graphics
      • 6.S982 Clinical Data Learning, Visualization, and Deployments

    • Spring25 New/Special

      • 6.4300 Intro to Computer Vision
      • 6.S041 Algorithmic and Human Decision-Making
      • 6.S899 Learning of Time Series with Interventions
      • 6.S954 Computer Vision and Planetary Health
      • 6.S963 Beyond Models – Applying Data Science/AI Effectively
      • 6.S966 Symmetry and its Application to Machine Learning and Scientific Computing
      • 6.S982 Diffusion Models: From Theory to Practice
      • 6.S988 Mathematical Statistics: A Non-Asymptotic Approach