Courses:

Course Info

• Course Number / Code:
• 6.867 (Fall 2006) 
• Course Title:
• Machine Learning 
• Course Level:
• Graduate 
• Offered by :
• Massachusetts Institute of Technology (MIT)
  Massachusetts, United States  
• Department:
• Electrical Engineering and Computer Science 
• Course Instructor(s):
• Prof. Tommi Jaakkola
  
  Teaching Assistants:
  Ali Mohammad
  Rohit Singh 
• Course Introduction:
•  
• 
  
  6.867 Machine Learning
  
  
  
  

  Fall 2006

  
  
  

  
  
  

  Course Highlights

  
  
  

  
  
  6.867 Machine Learning
  
  
  
  

  Fall 2006

  
  
  

  

  Robotic mannequin, "Manny", constructed at Pacific Northwest Laboratory. (Image is taken from Department of Energy's Digital Archive.)

  
  
  

  Course Description

  
  
  

  6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
  
  

  Recommended Citation

  
  
  

  For any use or distribution of these materials, please cite as follows:

  Tommi Jaakkola, course materials for 6.867 Machine Learning, Fall 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].

  
  
  

  Technical Requirements

  
  
  

  Special software is required to use some of the files in this course: .m, .dat, and .zip.