Introduction to Machine Learning Summer School

June 18, 2018 - June 29, 2018, Chicago, USA

http://www.ttic.edu http://cam.uchicago.edu https://www.nsf.gov/
chicago

 

The Introduction to Machine Learning Summer School is a two week program jointly organized by Toyota Technological Institute at Chicago (TTI Chicago) and the Committee on Computational and Applied Mathematics (CCAM) at Univeristy of Chicago as part of an NSF funded Research Training Group (RTG) grant.

This intensive two week summer school will follow the format of a short course on beginner level introduction to machine learning (see tentative schedule for specific topics). The program is aimed at advanced undergraduate and master students in computer science, mathematics, statistics, and related fields, as well as graduate students from other disciplines who would like to better understand and use machine learning.
As a result of participating in this program, a student is expected to

  1. understand the goals and capabilities of machine learning,
  2. have mathmatical tools to formalize machine learning problems, and
  3. build systems by implementing state-of-the-art techniques in machine learning.

Application

Prerequisites

The participants are not expected to have prior exposure to machine learning. However, the program will be most beneficial for participants with some programming experience, and familiarity with basic concepts in probability and statistics, calculus, and linear algebra. Some examples of such concepts include:

Registration

If you are interested in participating, please apply through the link below.
Participation costs for admitted applicants will be fully covered by the NSF RTG grant and no tuition will be charged. Participants are however expected to cover their own transportation and housing costs.

Application for this program is closed.

Schedule

Date Schedule
June 18, Monday 9am-9:30pm
9:30:00-10:55am
11:05-12:30pm
12:30-2:00pm
2:00-5pm
Course setup
Lecture 1.a: Introduction, supervised learning [Slides]
Lecture 1.b: Linear regression [Slides]
Lunch
Programming
June 19, Tuesday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-3:30pm
3:30-5:00pm
Lecture 2.a: Overfitting, model selection [Slides]
Lecture 2.b: Regularization, gradient descent [Slides]
Lunch
Programming
Invited Talk - Mathew Walter
June 20, Wednesday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-5:00pm
Lecture 3.a: Classification, logistic regression [Slides]
Lecture 3.b: Logistic regression cont., multi-class classification [Slides]
Lunch
Programming
June 21, Thursday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-3:30pm
3:30-5:00pm
Lecture 4.a: Maximum margin classifier/support vector machines [Slides]
Lecture 4.b: Support vector machines cont. [Slides]
Lunch
Programming
Invited Talk - Nathan Srebro
June 22, Friday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-5:00pm
Lecture 5.a: Generative models, naive Bayes classifier [Slides]
Lecture 5.b: Structured classification: hidden Markov models [Slides]
Lunch
Programming
June 25, Monday 9:00am-10:25am
10:30am-11:30am
11:30am-12:30pm
12:30pm-2:00pm
2:00pm-5:00pm
Lecture 6.a: Review of week 1, introduction to neural networks
Invited Talk - Greg Durett (also the TTIC colloquium talk)
Lunch
Lecture 6.b: Backpropagation
Programming
June 26, Tuesday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-5:00pm
Lecture 7.a: Optimization and regularization for deep learning
Lecture 7.b: Special neural network architectures
Lunch
Programming
June 27, Wednesday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-5:00pm
Lecture 8.a: Ensemble methods, boosting
Lecture 8.b: Unsupervised learning: Gaussian mixture models, expectation maximization
Lunch
Programming
June 28, Thursday 9:00am-10:25am
10:35am-noon
noon-1:00pm
1:00pm-3:30pm
3:30-5:00pm
Lecture 9.a: Unsupervised learning: K-means, PCA, metric learning
Lecture 9.b: Unsupervised learning: Generative adversarial networks
Lunch
Programming
Invited Tutorial - Karen Livescu
June 29, Friday 9:00am-noon
noon-2:00pm
Lecture 10: Review and conclusion
Group lunch

Invited Talks

matt nati greg-durett karen
Mathew Walter
TTI Chicago
Nathan Srebro
TTI Chicago
Greg Durett
UT Austin
Karen Livescu
TTI Chicago

People

Instructors

suriya karl mesrob
Suriya Gunasekar
TTI Chicago
Karl Stratos
TTI Chicago
Mesrob I. Ohannessian
TTI Chicago

Teaching Assistants

rebecca pedro kevin
Rebecca Kotsonis Pedro Savarese Kevin Stangl

Advisory Committee

greg mary nati
Gregory Shakhnarovich
TTI Chicago
Mary Silber
CCAM, UChicago
Nathan Srebro
TTI Chicago

Administration

zellencia
Zellencia Harris
CCAM Student Affairs Administrator, UChicago