Logistics

This page and the Course Calendar constitute the official syllabus for this class.

Course Information

Machine Learning and Data Mining
CMSC643 Fall 2018

  • Lecture Meeting Times
    Tuesdays 7:00pm-9:30pm CSI 2118

  • Instructor:
    Héctor Corrada Bravo
    Center for Bioinformatics and Computational Biology
    Department of Computer Science
    hcorrada@umiacs.umd.edu
    Office: 3114F Biomolecular Sciences Building
    Phone Number: 301-405-2481
    Office Hours: before and after class or by appointment

  • Communication:

    • For course related questions, use Piazza as indicated below.
    • For any other communication (e.g., absences accomodations etc.) email me including [CMSC643] in the message subject.

We will use the class Piazza site https://piazza.com/umd/fall2018/cmsc643/home for questions, discussion and announcements. Assignments and grades for the class will be posted to the class ELMS site: https://umd.instructure.com/courses/1253031 . In case of an extended emergency closure, announcements on policy and procedures will be posted to Piazza.

Textbook and Resources:

We will use the following textbooks:

  • Daume (2017) A Course in Machine Learning Book Site
  • Leskovec, et al. (2016). Mining of Massive Datasets. Book Site
  • Geron (2017) Hands-On Machine Learning wiht Scikit-Learn and TensorFlow. Safari
  • Chollet (2018) Deep Learning with Python. Manning

Other readings will be posted in the Course Calendar.

Expected outcomes and topics covered

Machine Learning methods have found their way into the modern data analyst’s toolbox. This course introduces popular methods with an emphasis on their practical usage for data analysis. Coverage of their statistical and computational theoretical underpinnings acquaints students with methods to evaluate statistical machine learning models defined in terms of algorithms or function approximations.

Topics covered include: regression and prediction, tree-based methods, overview of supervised learning theory, support vector machines, kernel methods, ensemble methods, clustering, visualization of large datasets, graphical models among others.

At the end of this course, students will be able to describe, implement and analyze algorithms that solve fundamental problems in Machine Learning. They will also be able to apply these methods to data in variety of applications.

Expectations for Students

Class prep

The Course Calendar will list readings (uploaded to ELMS). You are required to read this material before lecture

Announcements and discussion

We will use the Piazza page for class announcements. Please use the Piazza page for all discussion.

Other policies

  • There will be reading assignments. Students are expected to have read the material before class.
  • Students are expected to attend lectures. Active participation is expected. There will be graded work done in class.
  • Students will be expected to present project progress reports and papers during class.
  • Assignments are to be handed-in electronically or in class as instructed on their due date. Late assignments will not be accepted.
    Students may discuss homeworks and projects in groups. However, each student must write and/or program solutions independently.
  • Cell phone usage is prohibited during lecture, laptop use will be allowed to the extent that students demonstrably use it to follow along an in-class analysis or demonstration.

Grading Procedures

  • Data analysis projects (50%)
  • Written exercises (30%)
  • Final oral examination (20%)

University Policies and Resources

Policies relevant to Undergraduate Courses are found here: http://ugst.umd.edu/courserelatedpolicies.html. Topics that are addressed in these various policies include academic integrity, student and instructor conduct, accessibility and accommodations, attendance and excused absences, grades and appeals, copyright and intellectual property.

Course evaluations

Course evaluations are important and that the department and faculty take student feedback seriously. Students can go to http://www.courseevalum.umd.edu to complete their evaluations.