Department: Kahlert School of Computing
Semester: Fall 2025
Instructor: Kenneth Marino
Credits: 3.0
Schedule: Mo/We 3:00PM – 4:20PM
Location: WEB L101
Prerequisites: CS 2420 AND CS 2100 OR MATH 2200 AND MATH 2270
This course is designed to provide a groundwork for both machine learning and deep learning early on in undergraduate studies. Each lecture covers fundamental topics in Machine Learning interleaved with their practical application using Python and machine learning libraries such as PyTorch to implement and experiment with the discussed concepts.
Topics include training paradigms, loss functions, optimization, evaluation, hyperparameter tuning, generalization, simple neural networks, CNNs and Transformers, backpropagation, featurization, and more. The course will also cover topics in probability and linear algebra which are fundamental to understanding machine learning basics.
By the end of the course, students will be prepared to take more advanced courses to deepen their theoretical and applied knowledge of machine learning and deep learning.