
What is this course about?
Machine learning algorithms aim to "make sense of data", often
developing some generalizations that are useful for
future tasks, for example in classification, prediction, and control.
Machine learning has matured and expanded over the last two decades
and is now used extensively as a tool in many application areas.
Increasingly, theory and analysis are used to guide the development of successful algorithms and systems.
Computational Learning Theory investigates such tasks and algorithms
in a formal framework. The focus is to identify performance guarantees
for algorithms in terms of computational complexity (how long it takes
to run), data complexity (how much data is required), and convergence
properties (how well does the result/output of the learning algorithm
perform).
Alternatively,
when such guarantees do not exist, we might
aim to show that learning is impossible by proving
lower bounds on the amount of resources for any
potential algorithm.
Models vary from adversarial worst case scenarios,
to statistical settings where a random process generates the data, and
to interactive settings where the learner can control the flow of
data.
The course will mix taught classes with seminar type reading of
research papers of recent topics.
This semester: In addition to a review of core topics,
we will explore some of the following topics: online convex optimization, bandit
problems, PACBayes theorems, learning theory for pattern recognition
models, matrix completion problems.
Syllabus:
Probabilistic and adversarial models of machine learning. Development and analysis of machine learning principles and algorithms, their computational complexity, data complexity and convergence properties. Computational and cryptographic limitations on algorithms for machine learning. Core results and recent developments in this field.
Prerequisites: COMP 160; EE 104 or MATH 162; COMP 170 recommended but not required. Or permission of instructor.
Times and Location:
(D+ Block)
TR 10:3011:45,
Lane Hall 100A
Instructor:
Roni Khardon
Office: Halligan 230
Office Hours: Wednesday 56pm
Phone: 16176275290
Email: roni@cs.tufts.edu
Policy on collaboration on homework assignments: You may discuss the problems and general ideas about their solutions with other students, and similarly you may consult other textbooks or the web. However, you must work out the details on your own and write the solution on your own. Every such collaboration (either getting help of giving help) and every use of text or electronic sources must be clearly cited and acknowledged in the submitted homework. Failure to follow these guidelines will result in disciplinary action for all parties involved. Any questions? for this and other issues concerning academic integrity please consult the booklet available from the office of the Dean of Student Affairs.
Type  What  Due Date 
Reading  [KV] Sections 1.11.3; [SB] Chapters 2,3  
Lecture 1  Introduction to CLT  
Lecture 2  Learning Intervals; PAC Learning Model  
Assignment 1  Assignment 1  9/24 
Lecture 34  Learning conjunctions; Tools for Probabilistic Analysis  
Reading  [KV] Chapter 9; [SB] Appendix B  
Reading  [KV] Sections 4.12 (Optional: skim through 4.3.14.3.2)  
Lecture 45  Variations of PAC learning; Proof of Hoeffding's Bound;  
Assignment 2  Assignment 2  10/6 
Reading  [KV] Sections 1.41.5  
Lecture 6  Hardness Results for PAC learning  
Reading  [KV] Chapter 2; see also section 7.3, and Chapter 10 of [SB]  
Lecture 7 
Occam's Razor; Compression schemes
The companion slides and pages 1213 of this paper 

Reading  [SB] Sections 4.12 and 7.2  
Lecture 89  Agnostic Learning and Structural Risk Minimization  
Reading  [KV] Ch 3; [SB] Ch 6.  
Lecture 1011 
The VC dimension and Learnability
(properties and lower bounds)
Lecture notes from previous offering of the course 

Assignment 3  Assignment 3  10/27 
Lecture 1314 
Convergence based on VC Dimension
Lecture notes from previous offering of the course we also used companion slides and for the Agnostic case we followed Avrim Blum's notes 

Reading  [SB] 26.1, 28.1  
Lecture 1516 
Convergence Based on Rademacher Averages
We used companion slides 

Reading  [SB] Chapters 12,13  
Lecture 1617 
Convergence for Convex Learning Problems
We used companion slides 

Assignment 4  Assignment 4  12/3 
Lecture 1720 
On line learning and Online Convex Optimization
Lecture notes from previous offering of the course L1 , L2 , L3 , L4 

Sources for Online Convex Optimization Lectures 
Lectures follow material from Kakade and Tewari lecture notes L3, and L4 and the survey article By ShalevSchwartz 

Lecture 2124  Paper Presentations 