Inmas Machine Learning Workshop 2023

Welcome to the website of the Inmas Workshop on Machine Learning 2023. 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.

Location

All sessions will be held in the Gather room that you can access using the link provided in the announcement e-mail.

Workshop Schedule

As a preparation for the workshop, we encourage you to complete the pre-work before the first session on Saturday, January 14.

Friday, January 13

• 8:00 PM Eastern Time (ET) / 7:00 PM Central Time (CT):
  (Optional) Office Hour: Feedback & help with pre-work

Saturday, January 14

Morning Session (Session I)

• 10:00 AM - 1:00 PM ET (9:00 AM - 12:00 PM CT):
  Framework of Statistical Learning, Regularization, High-Dimensional Data

Afternoon Session (Session II)

• 2:30 PM - 5:30 PM ET (1:30 PM - 4:30 PM CT):
  Classification Problems, Natural Language Processing

Sunday, January 15

Morning Session (Session III)

• 10:00 AM - 1:00 PM ET (9:00 AM - 12:00 PM CT):
  Principal Component Analysis, Clustering

Afternoon Session (Session IV)

• 2:30 PM - 5:30 PM ET (1:30 PM - 4:30 PM CT):
  Neural Networks and Deep Learning

Instructor: Christian Kümmerle, University of North Carolina at Charlotte. Contact: kuemmerle@uncc.edu
Teaching Assistants: Benjamin Brindle, Derek Kielty, Yuxuan Li, Emily Shinkle, Yashi, Sukurdeep, Tim Wang

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.