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9.5 .520/6.860: : Statistical Learning Theory ry and Applications Class: Tue, Thu 11:00 - 12:30 pm , 46-3002 (Singleton) Office Hours: Friday 1:00 pm - 2:00 pm, 46-5156 (Poggio lab lounge) and/or 46-5165 (MIBR Reading Room) Web:

  1. 9.5 .520/6.860: : Statistical Learning Theory ry and Applications • Class: Tue, Thu 11:00 - 12:30 pm , 46-3002 (Singleton) Office Hours: Friday 1:00 pm - 2:00 pm, 46-5156 (Poggio lab lounge) and/or 46-5165 (MIBR Reading Room) • Web: http://www.mit.edu/~9.520/ • Contact: 9.520@mit.edu • Mailing list: 9.520students@mit.edu (?) • Live Stream: CBMM Youtube channel • 9.520/6.860 will use Stellar • Also check web (announcements) for updates

  2. Material Slides — will be posted (for most Videos — check CBMM Notes — lectures) on the website L. Rosasco and T. Poggio, Machine Learning: a Regularization Approach, MIT-9.520 Lectures Notes , Manuscript, (will be provided) For feedback on book (typos, errors, ...) https://goo.gl/forms/pQcewnsAV3lCNoyr1

  3. Faces ● Instructors:

  4. Faces • Instructors: • Lorenzo Rosasco

  5. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin

  6. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin • Tomaso Poggio

  7. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin • Tomaso Poggio • Andy Banburski (also head TA?)

  8. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin • Tomaso Poggio • Andy Banburski (also head TA?) • TAs: • Michael Lee

  9. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin • Tomaso Poggio • Andy Banburski (also head TA?) • TAs: • Michael Lee • Qianli Liao

  10. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin • Tomaso Poggio • Andy Banburski (also head TA?) • TAs: • Michael Lee • Qianli Liao • Morteza Sarafyazd

  11. Faces • Instructors: • Lorenzo Rosasco • Sasha Rakhlin • Tomaso Poggio • Andy Banburski (also head TA?) • TAs: • Michael Lee • Qianli Liao • Morteza Sarafyazd • Abhimanyu Dubey

  12. Syllabus at t a gla lance

  13. Problem sets (0.6) • 4 problem sets (0.15 each) • 2 - 3 questions (exercises and/or MATLAB) • 1 week due • Late policy on next slide • typeset in LaTeX (template will be provided) Grading • Online submission by due date policies Project (0.3) • See later Participation (0.1) • Attending class lectures is required! • Sign-in sheet will be circulated on random lectures

  14. • Problem sets (0.6) • 4 problem sets (0.15 each) • 2 - 3 questions (demonstrations/exercises + short MATLAB) • 7 days due! • typeset in LaTeX (template provided) • online submission by due date • Late policy • All students have 4 free late days (to be used on psets and project proposal) • You may use up to 2 late days per assignment with no penalty Problem sets • Beyond this, we will deduct a late penalty of 50% of the grade per additional late day • Dates (due times are 11:59 pm). Submission online (on Stellar). Problem Set 1 , out: Sep. 19, due: Wed., Sep. 25 (Class 07). Problem Set 2 , out: Oct. 03, due: Wed., Oct. 09 (Class 10). Problem Set 3 , out: Oct. 31, due: Wed., Nov. 06 (Class 18). Problem Set 4 , out: Nov. 14, due: Wed., Nov. 20 (Class 21). • Collaboration policy: You may discuss with others but need to work out your own solution.

  15. Theory Algorithms Application • This is not a data science Projects Review course, so we will not consider data preparation as contributing to the grade. Dates • Abstract and title: Nov. 1 report (NIPS format): 5 • Feedback and approval: Nov. pages + references 8 • Report submission: Dec. 11

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