CS 2750 Machine Learning

CS2750 Machine Learning (ISSP 2170)

Time: Tuesday, Thursday 1:00pm-2:15pm
Location: Sennott Square, Room 5313

Instructor: Milos Hauskrecht
Computer Science Department
5329 Sennott Square Bldg
phone: x4-8845
e-mail: milos at cs pitt edu
office hours: Tuesday, Thursday: 3:00-4:30pm,

TA: Huihui Xu
5108 Sennott Square Bldg
phone: 412-624-5757
e-mail: huihui. xu at pitt edu
office hours: Monday: 1:30pm-3:00pm, Wednesday: 11:15am-12:45pm

Announcements !!!!!

There is no final exam for the course, only the final project. The final project reports are due on April 23, 2020 at 1:00pm. Please see the guidelines for preparing the reports and programs before the submission .
The final project presentations will be held on zoom on April 21, 2020 and April 23, 2020 during the regular class time at 1:00pm-2:15pm. Please see instructions for preparing the presentations and the order of presentations on April 21, 2020 and April 23, 2020.
Homework assignment 10 is out. It will be due on April 16, 2020. The programs for the assignment are here.
All lectures starting March 23, 2020 are recorded and available online via CourseWeb and Zoom.
Office hours (for both the TA and the instructor) are held online on Zoom platform during the regularly scheduled office hour times. Access info for the office hours is now available on the CourseWeb.
Rules for submission of assignments
Course syllabus

Here are brief reviews of:

Basic Math for ML and
Basics of probabilities for ML , and
a Matlab tutorial and Matlab tutorial files

Links

Course description
Lectures
Homeworks
Term projects
Matlab

Abstract

The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt to their environments. Learning techniques and methods developed by researchers in this field have been successfully applied to a variety of learning tasks in a broad range of areas, including, for example, text classification, gene discovery, financial forecasting, credit card fraud detection, collaborative filtering, design of adaptive web agents and others.

This introductory machine learning course will give an overview of models and algorithms used in machine learning, including linear regression and classification models, multi-layer neural networks, support vector machines, Bayesian belief networks, mixture models, clustering, ensemble methods, and reinforcement learning. The course will give the student the basic ideas and intuition behind these methods, as well as, a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning solutions on various datasets during homework assignments and in context of a term project.

Course syllabus

Prerequisites

Knowledge of calculus, linear algebra, probability (CS 1151), statistics (CS 1000), and programming (CS 1501) or equivalent, or the permission of the instructor.

Textbook:

Chris Bishop. Pattern recognition and Machine Learning Springer, 2006.

Other ML books:

Hal Daume III. A course in Machine Learning , available online
K. Murphy. Machine Learning: A probabilistic perspective, MIT Press, 2012.
G. James, D. Witten,T. Hastie, R. Tibshirani. An introduction to Statistical Learning: with Application to R. Springer, 2017.
J. Han, M. Kamber. Data mining. Concepts and Techniques. 3rd ed. Morgan Kauffman.
T. Mitchell. Machine Learning. Mc Graw Hill, 1997.

Lectures


Lectures	Topic(s)	Assignments
January 7, 2020	Administration. Introduction to Machine Learning. Readings: Bishop: Chapter 1 Daume: Chapters 1 and 2	.
January 9	Designing a learning system Readings: Bishop: Chapter 1, Daume: Chapters 1 and 2	.
January 14, 2020	Designing a learning system II Readings: Bishop: Chapter 1, Daume: Chapters 1 and 2	.
January 16, 2020	Matlab tutorial Readings: Tutorial files	Homework assignment 1 ( Data for the assignment) (
January 21, 2020	Density estimation I Readings: Bishop Chapter 2.	.
January 23, 2020	Density estimation II Readings: Bishop Chapter 2.	Homework assignment 2 ( Data for the assignment)
January 28, 2020	Density estimation III Readings: Bishop Chapter 2.	.
January 30, 2020	Linear regression Readings: Bishop Chapter 3.	Homework assignment 3 ( Data for the assignment)
February 4, 2020	Linear regression (cont) Readings: Bishop Chapter 3.	.
February 6, 2020	Linear models for classification Readings: Bishop Chapter 4.	Homework assignment 4 ( Data for the assignment)
February 11, 2020	Linear models for classification, Evaluation of classifiers Readings: Bishop Chapter 4.	.
February 13, 2020	Support vector machines Readings: Bishop: Chapter 7.1.	Homework assignment 5 ( Data for the assignment)
February 18, 2020	Multilayer neural networks Readings: Bishop: Chapter 5.1-3, 5.5.	.
February 20, 2020	Multiclass classification. Decision trees. Readings Bishop: Chapter 14.4. :	Homework assignment 6 ( Data for the assignment)
February 25, 2020	Bayesian belief networks Readings: Bishop: Chapter 8.1-2	.
February 27, 2020	Bayesian belief networks Readings Bishop: Chapter 8.1-2 :	Homework assignment 7 ( Data for the assignment)
March 3, 2020	Graphical models: Bayesian belief networks: inference, Markov Random Fields Readings Bishop: Chapter 8.3-4:	.
March 5, 2020	Midterm exam Readings Bishop:	.
March 24, 2020	Expectation-maximization. Recording of the lecture now available via CourseWeb Online class: discussion and Q/A session at 1:00pm on Zoom Readings Bishop: Chapter 9.2	.
March 26, 2020	Clustering Recording of the lecture now available via CourseWeb Online class: discussion and Q/A session at 1:00pm on Zoom Readings Bishop: Chapter 9.1	Homework assignment 8 ( Data for the assignment)
March 31, 2020	Feature selection, Dimensionality reduction Recording of the lecture now available via CourseWeb Online class: discussion and Q/A session at 1:00pm on Zoom Readings Bishop: Chapter 12.1.	.
April 2, 2020	Learning with multiple models: mixture of experts, bagging, boosting Recording of the lecture now available via CourseWeb Online class: discussion and Q/A session at 1:00pm on Zoom Readings Bishop Chapter 14	Homework assignment 9 ( Data for the assignment)
April 7, 2020	Reinforcement learning I Recording of the lecture now available via CourseWeb Online class: discussion and Q/A session at 1:00pm on Zoom Readings: Kaelbling, Littman, Moore. Reinforcement Learning: a survey	.
April 9, 2020	Reinforcement learning II Recording of the lecture now available via CourseWeb Online class: discussion and Q/A session at 1:00pm on Zoom Readings: Kaelbling, Littman, Moore. Reinforcement Learning: a survey	Homework assignment 10 ( Programs for the assignment)

A link to CS 2750 lecture notes from 2019 .

Grading (updated in April 2020):

Homework assignments 50%
Midterm exam 25%
Final project 25%

Homeworks

The homework assignments will consist of a mix of theoretical and programming problems Programming assignments will require you to implement in Matlab some of the learning algorithms covered during the lectures and experiment with them on various real-world datasets. See rules for the submission of assignments.

The assignment reports and programs should be submitted electronically via Course web. The assignments are due at the beginning of the class on the day specified on the assignment. In general, no extensions will be granted.

Collaborations: No collaboration on homework assignments, programs, and exams is permitted unless you are specifically instructed to work in groups.

Term projects

The term project is due at the end of the semester and accounts for a significant portion of your grade.

Matlab

Matlab is a mathematical tool for numerical computation and manipulation, with excellent graphing capabilities. It provides a great deal of support and capabilities for things you will need to run Machine Learning experiments. The CSSD at UPitt offers free student Matlab licenses. To obtain the licence please check the following link to the Matlab CSSD page . In addition, Upitt has a number of Matlab licences running on both unix and windows platforms.

Matlab resources on the web:

Online MATLAB documentation
Online Mathworks documentation including MATLAB toolboxes

Cheating policy: Cheating and any other anti-intellectual behavior, including giving your work to someone else, will be dealt with severely and will result in the Fail (F) grade. If you feel you may have violated the rules speak to us as soon as possible. Please make sure you read, understand and abide by the Academic Integrity Code for School of Computing and Information (SCI) .

Students With Disabilities:
If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890 as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Last updated by Milos on 01/06/2020