Overview

How does Netflix learn what movies a person likes? How do computers read handwritten addresses on packages, or detect faces in images? Machine learning is the practice of programming computers to learn and improve through experience, and it is becoming pervasive in technology and science. This course will cover the mathematical underpinnings, algorithms, and practices that enable a computer to learn. Topics will include supervised learning, unsupervised learning, evaluation methodology, and Bayesian probabilistic modeling. Students will learn to program in Python and apply course skills to solve real-world prediction and pattern recognition problems. Programming intensive.

Instructor Dan Sheldon
dsheldon (at) mtholyoke.edu
Lecture Tuesday, Thursday 11:30am–12:45pm
Fourth Hour Friday 3:15pm–4:05pm
Location Carr 102
Piazza https://piazza.com/mtholyoke/fall2016/cs335
Moodle https://moodle.mtholyoke.edu/course/view.php?id=10046
Textbook none
Office Hours (tentative) Tuesday 4–5, Thursday 1–2, Clapp 200, or by appointment

Prerequisites

The goal of these prerequisites is to ensure that you are: comfortable programming in some language; familiar with basic CS paradigms; know elementary probability and calculus; and are generally comfortable with mathematical tools and reasoning.

Resources

There is no required textbook for this course. Here are some useful resources:

  1. An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani: accessible undergraduate ML textbook with statistics focus. Freely downloadable.
  2. Introduction to Machine Learning by Alpaydin: approachable undergraduate ML text with CS focus.
  3. Artificial Intelligence: A Modern Approach by Russell and Norvig: the most widely-used AI textbook. Chapters 13–15, 18, and 20 cover material related to machine learning.
  4. Pattern Recognition and Machine Learning by Bishop. Graduate / advanced undergraduate level ML text with a probabilistic / Bayesian focus.
  5. Machine Learning: a Probabilistic Perspective by Murphy. Comprehensive new ML textbook at graduate / advanced undergraduate level.
  6. The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman. Graduate level statistical view of many machine learning topics. Freely downloadable.
  7. Coursera Machine Learning course by Andrew Ng: outstanding free online ML course.
  8. Course handouts from Stanford CS 229 by Andrew Ng

The books above that are not freely downloadable (Alpaydin; Russell and Norvig; Bishop; Murphy) are on three-hour reserve at MHC library.

Python

Programming assignments will use Python, NumPy, and SciPy. The required Python environment is the Anaconda 4.1.1 distribution of Python 2.7. We will not help grade or debug work unless you are working in this environment. Anaconda 4.1.1 isinstalled on lab computers in Clapp 202 and Kendade 307. You are also encouraged to download and install it on your personal computer.

Please see this page on getting started with Python for CS 335.

Here are some general / comprehensive resources on Python and SciPy:

Here are some more focused Python resources oriented toward a class like ours:

Course Objectives

The goals of the course are

Like many ML courses, this one is organized primarily as a sequence of specific techniques (see the schedule), which comprise a small subset of the available machine learning algorithms. We will learn about details of these specific techniques and also use them to explore cross-cutting concepts:

The skills learned in this class will prepare the student to explore much more widely within the field of machine learning.

Policies

The coursework will consist of:

The grading breakdown is:

Homework

Homework will be assigned and due every 1–2 weeks during the first part of the semester. They will be a mix of written problems, programming exercises, and experiments. Later in the semester, assigned work will shift toward the final project.

Homework submission

All homework will be submitted electronically through moodle. Written work should be scanned and submitted as a pdf. Specific instructions will accompany each assignment.

Late policy

Collaboration

Collaboration on assignments is encouraged. However, every student must write their own code, run their own experiments, and write their own solutions. Sharing of code or written solutions will be considered a violation of the honor code. Also, I highly encourage each student to first attempt problems on their own, especially for the shorter exercises that are designed to test and reinforce concepts taught in class. Please write the names of all collaborators at the beginning of the written portion of the submission.

Course Project

Students will work as individuals or in small groups on a final project. This can be either a hands-on application of machine learning algorithms learned in class to an interesting data set, or an in-depth exploration of a machine learning topic not covered in this class. Details will be announced later in the course.

Piazza

We will use Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and the instructor. Rather than emailing questions, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find our class page at: https://piazza.com/mtholyoke/fall2016/cs335

Students are encouraged to help answer each other’s questions on Piazza. I will also monitor the discussion and answer questions. So, you are likely to get an answer very quickly. The official policy is that the instructor will read and respond (as necessary) to new posts within 24 hours on weekdays, and 48 hours on the weekend. I will not respond after 9pm.

Participation

Participation includes arriving on time to class and required fourth hours, engaging meaningfully in lecture activities (e.g., peer discussions or exercises), giving a project presentation, and contributing in the way you are most comfortable to course discussions during lecture or on the class forum.

Accommodations

If you have a disability and would like to request accommodations, please contact AccessAbility Services, located in Wilder Hall B4, at (413) 538-2634 or accessability-services@mtholyoke.edu. If you are eligible, they will give you an accommodation letter which you should bring to me as soon as possible.