CS074/CS174 | Machine Learning and Statistical Data Analysis

Course description

This course provides an introduction to statistical modeling and machine learning. Topics include learning theory, supervised and unsupervised machine learning, statistical inference and prediction. A wide variety of algorithms will be presented, including K-nearest neighbors, naive Bayes, decision trees, support vector machines, logistic regression, K-means, mixtures of Gaussians, principal components analysis, Expectation Maximization. The course will also discuss modern applications of machine learning such as image segmentation and categorization, speech recognition, and text processing.

Administrative information

Instructor: Lorenzo Torresani | Sudikoff 109 | office hours: Wednesdays 1-3pm
Teaching assistants: Suman Bera | office hours: Thursdays 2-4pm in Sudikoff 212; Jun Han | office hours: Fridays 2-4pm in Sudikoff 202; Andy Sarroff | office hours: Tuesdays 2-4pm in Sudikoff 212
Course staff email: cs174@cs.dartmouth.edu
Lectures: Tue&Thu 10-11:50am | x-hour (used occasionally to make up cancelled classes) Wed 3-3:50
Life Sciences Center 100
Lab: Sudikoff 001: Linux machines with Matlab. As an alternative, you can use Matlab on your machine by following the instructions provided here.
Textbook (recommended but not required): Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2006

Grading and policies

Grading scheme

The course grade will be based 36% on the homework assignments (each of the three homework assignments will count for 12% of the final grade), 32% on the midterm exam and 32% on the final exam. The homework assignments will require answering questions and implementing some algorithms in Matlab, but prior knowledge of Matlab is not required. During an x-hour we will present a tutorial covering the basics of Matlab.

Late homeworks

Each student has 3 free late days to be used over the course of the term as he/she likes. Once these days are used up, any homework turned in late will be penalized 25% per late day. No exception! Any portion of a late day is counted as one full day. Assignments are typically due at 11:59 pm of the due date. The code portion of each homework submission must be turned in via Canvas. The answers to technical questions must be provided in paper form and dropped in the course mailbox near the Sudikoff entrance.

Homework and Exam Schedule

Homework 1: out on 4/12/2016; due on 4/28/2016.

Homework 2: out on 4/28/2016; due on 5/12/2016.

Homework 3: out on 5/12/2016; due on 5/26/2016.

Midterm exam: 5/5/2016 10:00am-11:50am.

Final exam: 6/3/2016 3pm-5pm.

No-laptop policy

We have a no-laptop policy in class (texting, sleeping or engaging in other activities unrelated to the lecture is also forbidden). This policy will be strictly enforced so as to encourage active participation by all students and to avoid distracting people that are focusing on the lecture. Class attendance is highly recommended and considered necessary to do well in the course. However, it is not mandatory. If you come to class you are expected to obey this policy. A penalty of 5% will be applied to the final grade every time this policy is violated.

Auditing

Please contact the instructor if you would like to audit the course.

Academic integrity

You may discuss the assignments with other current CS074/174 students, but your submission must be entirely your own work. That is, your code and any other solutions you submit must be created, written/typed, and documented by you alone. You may not copy anything directly from another student's work. For example, memorizing or copying onto paper a portion of someone else's solution would violate the honor code, even if you eventually turn in a different answer. Similarly, e-mailing a portion of your code to another student, or posting it on-line for them to see would violate the honor code. We do encourage discussion of assignments between students, subject to these rules.

You cannot make use of any code taken from outside references for your homework assignments, unless explicitly authorized to do so by the instructor. As a rule of thumb, you should treat any external code as software written by another CS074/174 student: you are not allowed to copy it or to use it as a template to implement your solution.

You cannot collaborate or copy in any way during the exams. The exams will be will be closed-book, closed-notes, closed laptop.

These rules will be strictly enforced and any violation will be treated seriously