MLSS 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

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

Program Dates: June 18, 2018 - June 29, 2018, Chicago, USA
Location: TTI Chicago, 6045 S Kenwood Avenue, Chicago, IL 60637
Contact: intro-mlss2018@ttic.edu

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:

Random variables (continuous or discrete), expectation, mean/variance.
Derivatives, partial derivatives, gradients.
Matrix and vector operations.
Programming: Familiarity with data structures and algorithms.

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

Slides and materials adapted from lectures/slides of David McAllester (TTIC), Greg Shaknarovich (TTIC), David Sontag (MIT), and Nati Srebro (TTIC).

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. Instructor: Suriya Gunasekar. [Slides] Lecture 1.b: Linear regression. Instructor: Karl Stratos. [Slides] Lunch Programming Additional resources: Python numpy tutorial
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. Instructor: Suriya Gunasekar. [Slides] Lecture 2.b: Regularization, gradient descent. Instructor: Karl Stratos. [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. Instructor: Suriya Gunasekar. [Slides] Lecture 3.b: Logistic regression continuation, multi-class classification. Instructor: Karl Stratos. [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. Instructor: Suriya Gunasekar. [Slides] Lecture 4.b: Support vector machines continuation. Instructor: Karl Stratos. [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. Instructor: Suriya Gunasekar. [Slides] Lecture 5.b: Structured classification: hidden Markov models. Instructor: Karl Stratos. [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. Instructor: Suriya Gunasekar. [Slides] Invited Talk - Greg Durett (also the TTIC colloquium talk) Lunch Lecture 6.b: Backpropagation. Instructor: Karl Stratos. [Slides] Programming Additional resources: (a) One of several notes on backpropagation, (b) Backpropagration demo
June 26, Tuesday	9:00am-10:25am 10:35am-noon noon-1:00pm 1:00pm-3:30pm 3:30-5:00pm	Lecture 7.a: Optimization and regularization for deep learning. Instructor: Suriya Gunasekar. [Slides] Lecture 7.b: Special neural network architectures. Instructor: Karl Stratos. [Slides] Lunch Programming Invited Tutorial - Karen Livescu Additional resource: (a) Notes on (S)GD variants (b) Demo to play with , (c) CNN lecture notes , (d) RNN notes
June 27, Wednesday	9:00am-10:25am 10:35am-noon noon-1:00pm 1:00pm-5:00pm	Lecture 8.a: Encoder decoder architectures in NN. Instructor: Karl Stratos.[Slides] Lecture 8.b: Ensemble methods, boosting. Instructor: Suriya Gunasekar. [Slides] Lunch Programming
June 28, Thursday	9:00am-10:25am 10:35am-noon noon-1:00pm 1:00pm-5:00pm	Lecture 9.a: Unsupervised learning: dimensionality reduction. Instructor: Suriya Gunasekar. [Slides] Lecture 9.b: Unsupervised learning: EM algorithm. Instructor: Karl Stratos. [Slides] Lunch Programming
June 29, Friday	9:00am-10:30am 10:30am-noon noon-2:00pm	Lecture 10a: Unsupervised learning: Clustering. Instructor: Karl Stratos. [Slides] Lecture 10b: Review and conclusion. Instructor: Suriya Gunasekar. [Slides] Group lunch