Machine Learning Foundations

Catalog description

This course provides a basic introduction to the machine learning and deep learning pipeline and concepts. Topics covered include: Machine learning uses and applications; machine learning and deep learning algorithms; data set requirements; data annotation, and validation; data representation formats; features and feature representation and extraction; the vector space model; traditional machine learning algorithms; machine learning and deep learning algorithm programming; evaluation methods; introduction to deep learning algorithms such as convolutional neural networks, auto encoders, and deep reinforcement learning; statistical significance-based analysis of machine learning methods; and other Artificial Intelligence special topics. Prerequisite(s): ITS 14000 FOR LEVEL UG WITH MIN. GRADE OF C OR CS 12300 FOR LEVEL UG WITH MIN. GRADE OF C OR CIS 16600 FOR LEVEL UG WITH MIN. GRADE OF C Course Learning Outcomes 1. Understand the role and importance of machine learning and deep learning principles in Data Science 2. Implement machine learning and deep learning algorithms using a programming language and libraries 3. Demonstrate the use of machine learning and deep learning techniques and skills to solve Data Science problems. 4. Compare and contrast the various types of machine learning-based and deep-learning based methods and select the appropriate method to solve a specific data science problem. 5. Design and develop machine learning and deep learning-based systems 6. Demonstrate the implementation of machine learning and deep learning systems using open-source tools. View Class Schedule