CMSC 478: Introduction to Machine Learning Instructor: Fereydoon Vafaei
This course covers fundamental concepts, methodologies, and algorithms related to machine learning. Topics covered include but not limited to:
- supervised and unsupervised learning
- model evaluation
- linear and logistic regression
- decision trees
- support vector machines
- random forests
- PCA
- neural networks
- deep learning (CNNs and RNNs)
Textbooks used for this course include:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron. This textbook is required.
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville. This textbook is recommended.
Warning to UMBC students: Students taking machine learning at UMBC with Dr. Vafaei: Presenting someone else’s work as your own in an assignment without proper citation of the source is an act of plagiarism. You are doing yourself a disservice by not learning from Dr. Vafaei, who is not only an expert on this topic but extremely passionate about teaching it too.