Welcome to the website of the Inmas Workshop on Machine Learning 2022. This website contains links and files to all relevant content of the workshop.

Overview

This course is designed to provide a glimpse at modern computational approaches for the analysis of data sets. We cover the concepts of supervised learning and unsupervised learning and illustrate the usage of some popular methods in by suitable toolboxes in Python.

As many data sets that are encountered in practice are inherently high-dimensional, we aim to gain intuition about the geometry of high-dimensional spaces and distributions, and shed light on computational aspects of some of the covered methods.

Workshop Schedule

As a preparation for the workshop, we encourage you to complete the pre-work before the first session on Saturday, January 15.

Friday, January 14

  • 8:00 PM ET: (Optional) Office Hour: Feedback & help with pre-work

Saturday, January 15

Morning Session (Session I)

  • 10:00 AM - 1:00 PM ET: Framework of Statistical Learning, Regularization, High-Dimensional Data

Afternoon Session (Session II)

  • 3:00 PM - 6:00 PM ET: Classification Problems, Natural Language Processing

Sunday, January 16

Morning Session (Session III)

  • 10:00 AM - 1:00 PM ET: Principal Component Analysis, Clustering

Afternoon Session (Session IV)

  • 2:00 PM - 5:00 PM ET: Neural Networks and Deep Learning

All sessions will be held on Gather.town at the link provided via e-mail.

Instructor: Christian Kümmerle (Johns Hopkins University)
Teaching Assistants: Lanlan Ji, Vittorio Loprinzo, Salma Tarmoun, Sichen Yang

After-Workshop Office Hours

  • Sunday, January 30, at 8 pm ET / 7 pm CT, on Gather.town.

Computational Tools

This workshop will use practice exercises that will make use of the Python language, which is widely used for data science and machine learning due its property as a general purpose programming language and its modularity, which has attracted the development of a variety of powerful libraries.

The most relevant libraries we will use are:

  • NumPy: Basic manipulation of vectors and matrices.
  • SciPy: Scientific computing, in particular useful for linear algebra, optimization, signal and image processing.
  • matplotlib: Visualization and plotting.
  • seaborn: Package for visaulization, more high-level than matplotlib.
  • scikit-learn: Implementations of a wide range of machine learning
  • PyTorch: Machine learning library, suitable for deep neural network models.