Introduction to Machine Learning Summer School

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


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.



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:


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 expected to cover their own transportation and housing costs.

We will start considering applications on May 7th, 2018, and will continue to admitting participants until we reach capacity.




Lectures: Mon-Fri 9am-noon
Lectures will follow topics typically covered in a standard machine learning course.
Assignments: Mon-Fri 2.5-4 hours
Participants will work in groups on programming assignments under the guidance of teaching assistants.
Invited Lectures: Mon-Wed-Fri 3:30pm-5pm
Invited speakers present tutorials on special topics in machine learning.

Tentative Lecture Plan

Week 1
  1. Introduction and basic workflow of implementing ML algorithms
  2. Regression: linear regression, feature selection, model selection, bias-variance tradeoff, gradient descent
  3. Classification 1: logistic regression, (stochastic) gradient descent
  4. Classification 2: support vector machines, kernels, ensemble methods
  5. Unsupervised learning: K-means, PCA, generative models, mixture of Gaussians, Expectation Maximization
Week 2
  1. Introduction to deep neural networks: differentiable programming, backpropagation, pretraining
  2. Optimization and regularization for deep learning
  3. Architecture design 1: skip connections, weight sharing, CNNs
  4. Architecture design 2: variable length inputs, sequence to sequence architectures
  5. Review and conclusion

Invited Lectures



Affiliate Instructor
  • Zellencia Harris, CCAM, Univeristy of Chicago