6.887 Machine Learning for Systems

SHARE:

Graduate Level -AAGS
Units: 3-0-9
Prereqs: see below
Instructors:  Profs. Mohammad Alizadeh (alizadeh@csail.mit.edu) and Tim Kraska (kraska@mit.edu)
Schedule:  MW2:30-4, room 32-124
 
Description
This subject counts as a subject in the Computer Systems concentration. Machine learning is poised to change how people design, operate, and analyze computer systems. This course introduces the emerging area of learning-based systems, with the goal to provide working experience in applying learning to system design and to prepare students for research in this field. Topics include automatic optimization of system parameters, learning-enhanced data structures and algorithms (e.g., indexes, sketches, compression, caching, scheduling), core techniques (e.g., reinforcement learning, bandit algorithms, deep learning) and their applications to systems and networking.  The course will include lectures, invited talks by experts, a semester-long project and paper, and hands-on labs designed to give experience with topics covered.
 
Prerequisites:

  • Programming: 6.009 (Fundamentals of Programming)
  • Data Structures: 6.006 (Introduction to Algorithms) or equivalent
  • Machine Learning: 6.008 (Introduction to Inference), 6.036 (Introduction to Machine Learning), 6.034 (Artificial Intelligence) or equivalent
  • Systems: 6.033 or equivalent