Introduction to Machine Learning

CMSC 478

Spring 2019

Contact Information

Instructor: Tim Oates, ITE-336, oates@cs.umbc.edu, x5-3082, Office hours: TBD

TA: Vamshi Nagabandi, ITE 353, nvamshi1@umbc.edu, Office hours: Monday 12PM - 2PM, Wednesday 12PM - 2PM

Course Mechanics

Grades will be based on a midterm exam, a final exam, a project, and homework assignments. Every time we finish a topic I'll hand out a homework on that topic, and the homework on the previous topic will be due. There will be 10 such homework assignments so you'll always be practicing what you learn in class. I don't expect any one homework to be a lot of work. But I think it's important to get hands-on experience with what you're learning in class and to get early and frequent feedback on your understanding of the material.

The weights on the various items is as follows:

All assignments (homeworks and the various components of the course project) must be turned in at the beginning of class on the date that they are due. I understand that students have many demands on their time that vary in intensity over the course of the semester. Therefore, you will be allowed 3 late days without penalty for the entire semester. You can turn in 3 different assignments one day late each, or one assignment 3 days late, and so on. Once these late days are used, a penalty of 25% will be imposed for each day (or fraction thereof) an assignment is late (25% for one day, 50% for two, 75% for three, and 100% for four or more).

Hard copy is required for all assignments. If you are not on campus for the start of class (what?!?!) and want to email your assignment to me or the TA to establish that it was done on time, that's OK. However, you must hand in hard copy before the assignment will be graded.

The project is meant to give students deeper exposure to some topic in machine learning than they would get from the lectures, readings, and discussions alone. Those projects that are most successful often blend the student's own research with machine learning, e.g. by applying machine learning techniques to a problem in some other area, or by bringing an insight from some other area to a problem in machine learning. However, projects need not involve ties with ongoing research. Many good projects in the past have investigated the application of existing algorithms to a domain/dataset of interest to the student, such as Texas Hold'em, the stock market, sporting events, and so on. Students can come up with their own project ideas or they can come see me and we'll brainstorm project ideas. information.

Projects may be done by individuals or teams of two people. However, teams of two will be expected to do significantly more work than what is expected of an individual project. More information on projects can be found here.

Textbook

It's hard to find a good text for an introductory course in machine learning. It turns out that Hal Daume down at UMCP is writing what looks like a very good text. The downside is that it is not complete, but the parts that we will cover are in good shape. The upside is that it's free! You can get it here: CIML.

Syllabus

This syllabus is subject to small changes.

Class
Date
Topic
Events/Readings
1 Tue  29 Jan Course overview, decision trees Chapter 1 of CIML
2 Thu  31 Jan Decision trees, over-/under-fitting, advice
3 Tue  5 Feb Geometry, k-nearest neighbor Chapter 2 of CIML
4 Thu  7 Feb k-nearest neighbor, k-means clustering HW 1 assigned
5 Tue  12 Feb Perceptron Chapter 3 of CIML
6 Thu  14 feb Perceptron HW 1 due
HW 2 assigned
7 Tue  19 Feb Logistic regression Slides, thanks to Tom Dietterich
8 Thu  21 Feb Logistic regression, practical issues Chapter 4 of CIML
HW 2 due
HW 3 assigned
9 Tue  26 Feb Practical issues
10 Thu  28 Feb Probabilistic learning Chapter 7 of CIML
Bayes Nets chapter
HW 3 due
HW 4 assigned
11 Tue  5 Mar Probabilistic learning
12 Thu  7 Mar Probabilistic learning Project proposal due
13 Tue  12 Mar Probabilistic learning
14 Thu  14 Mar Reinforcement learning Parts of chapters 3, 4, 5, and 6 of the RL book
HW 4 due
HW 5 assigned
Tue  19 Mar Spring Break
Thu  21 Mar Spring Break
15 Tue  26 Mar Midterm Review on content of classes 1 - 13
16 Thu  28 Mar Midterm Exam on content of classes 1 - 13
17 Tue  2 Apr Reinforcement learning
18 Thu  4 Apr Reinforcement learning
19 Tue  9 Apr Neural networks Chapter 11 of CIML
HW 5 due
HW 6 assigned
20 Thu  11 Apr Neural networks and deep learning
21 Tue  16 Apr Deep learning
22 Thu  18 Apr Linear methods Chapter 6 of CIML
HW 6 due
HW 7 assigned
23 Tue  23 Apr Linear methods
24 Thu  25 Apr Kernel methods HW 7 due
HW 8 assigned
25 Tue  30 Apr Kernel methods
26 Thu  2 May Ensemble learning Chapter 11 of CIML
HW 8 due
HW 9 assigned
27 Tue  7 May Ensemble learning
28 Thu  9 May Project presentations HW 9 due
Henry; Ferry; Lin; Richards; Genega; McCarter; Milani; Dunstan; Bradford; Bowen; Mann; Carver; Arminger; Hwang; Kempton; Gorelik; Little
29 Tue  14 May Project presentations Patel; Sprehe; Weiss; Korb; Kareem; Singh; Vance; Coleman; Mukhin; Ochoa; Dudley; Huganir; Gebremariam; Nettie; Nehman; Byerly; Miller
Thu  16 May Final Exam 10:30AM - 12:30PM
Thu  23 May Final project writeup due