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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
- 6.790 [6.867] Machine Learning
- 6.796 Deep 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 (NEW)
- 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.C01 Modeling with Machine Learning: from Algorithms to Applications
- 6.3730 Statistics, Computation and Applications
- 6.7930 [6.871] Machine Learning for Healthcare
- 6.8200 [6.484] Sensorimotor Learning
- 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
- Fall24 New/Special
- 6.C40/24.C24 Ethics of Computing
- 6.S890 Topics in Multiagent Learning
- 6.S951 Modern Mathematical Statistics
- 6.S978 Deep Generative Models
- General