Comp 135: Introduction to Machine Learning
Department of Computer Science
Tufts University
Fall 2014

Course Web Page (this page):

  • Course information, Reading, Assignments, etc updated regularly in the table below

What is this course about?

Machine learning is the science of collecting and analyzing data and turning it into predictions, encapsulated knowledge, or actions. There are many ways and scenarios by which data can be obtained, many different models and algorithms for data analysis, and many potential applications. In recent years machine learning has attracted attention due to commercial successes and widespread use.
The course gives a broad introduction to machine learning aimed at upper level undergraduates and beginning graduate students. Some mathematical aptitude is required, but generally speaking we focus on baseline algorithms, practical aspects, and breadth and leave detailed analysis to more advanced courses: Statistical Pattern Recognition , Computational Learning Theory , Learning, Planning and Acting in Complex Environments .


An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.


Comp 15 and COMP/MATH 22 or 61 or consent of instructor. (Comp 160 is highly recommended).

Class Times:

Monday and Wednesday, 4:30-5:45, Halligan Hall 111A


Roni Khardon
Office Hours: Tuesday 4:30-5:30, and Mon/Wed 5:45-6:15 (after lecture)
Office: Halligan 230
Phone: 1-617-627-5290

Teaching Assistants:

Course Work and Marking

The course grade will be determined by a combination of
Written homework assignments (20%)
these assignments exercise and reinforce class material.
Experimental/Programming projects (30%)
these assignments exercise and reinforce class material. Projects will include both programming assignments and use of existing machine learning software.
Rules for late submissions:
All work must be turned in on the date specified. Unless there is a last minute emergency, please notify me of special circumstances at least two days in advance. Otherwise, If you haven't finished an assignment, turn in what you have on the due date, and it will be evaluated for partial credit.
In-class midterm exam (20%), Wednesday, October 22.
Final exam (30%) , scheduled according to K Block schedule on Wednesday, December 17, 3:30-5:30.
Note: If your final exam grade is higher than the midterm the midterm is discounted and the final will count for 50%.


On homework assignments and projects: 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 code/write-out 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.
On exams: no collaboration is allowed.
Failure to follow these guidelines may 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.

Tentative List of Topics

[We may not cover all sub-topics]

Textbooks and Material Covered

No single text covers all the material for this course at the right level. We have the following texts on reserve in the library. Other material will be selected from research and survey articles or other notes. Detailed reading assignments and links to material will be posted.


Reading, References, and Assignments

Topic Reading/Assignments Due Date
Introduction to Machine Learning Read the introductory chapter of [M], [WF], [F] or [A]
See also lecture slides.
week 1
Supervised Learning Basics:    
Instance based learning [M] Chapter 8 is cloest to class material; or [RN] 18.8; or [DHS] 4.4-4.6.
Andrew Moore's tutorial on kd-trees
See also lecture slides.
Decision Trees [M] Chapter 3; or [RN] 18.1-4; or [F] Chapter 5.
See also lecture slides.
Optional Reading T. Dietterich, M. Kearns, and Y. Mansour Decision Tree Learning and Boosting Applying the Weak Learning Framework to Understand and Improve C4.5. International Conference on Machine Learning, 1996.  
Empirical/Programming Assignment 1 Assignment 1 and corresponding Data 9/17
Written Assignment 1 Assignment 1 9/22
Naive Bayes Algorithm [M] 6.1-6.2, and 6.9-6.10; [DHS] Section 2.9; [F] 9.2; [WF] 4.2; See also new book chapter from [M]
See also lecture slides.
Linear Threshold Units [M] 4.1-4.4; DHS 5.5; See also new book chapter from [M]
See also lecture slides.
Features (selection, transformation, discretization) Wrappers for Feature Subset Selection Ron Kohavi, George H. John Artificial Intelligence, 1996. (Read till section 3.2 inclusive.)
Supervised and unsupervised discretization of continuous features. James Dougherty, Ron Kohavi, and Mehran Sahami. International Conference on Machine Learning, 1995.
See also lecture slides.
Evaluating Machine Learning Outcomes [M] Ch 5; [F] Ch 12
See also lecture slides.
Optional Reading Foster Provost, Tom Fawcett, Ron Kohavi The Case Against Accuracy Estimation for Comparing Induction Algorithms Proc. 15th International Conf. on Machine Learning, 1998.
T. Dietterich, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms Neural Computation 10(7), 1998.
Stephen Salzberg On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach Data Mining and Knowledge Discovery, 1997.