| |
Nov 21, 2024
|
|
|
|
|
CSC 6621 - Applied Machine Learning4 lecture hours 0 lab hours 4 credits
Course Description
This course expands on the introduction to data science and machine learning in CSC 5610 with coverage of deep learning and applied artificial intelligence. Students will be introduced to deep learning, applications to complex data such as images and text, and appropriate libraries. Students will explore the paradigm of artificial intelligence through concepts such as agent-based frameworks, planning problems, algorithms such as reinforcement learning, and applications to playing games. Students will participate in a seminar-like experience where primary literature is read and presented. A final project will allow students to explore topics and applications beyond those presented in class. Students will reinforce their learning of machine learning algorithms with hands-on laboratory exercises for development of representative applications.
Prereq: (CSC 5610 or CSC 2621) and (MTH 2130 or MTH 2340 or MTH 5810 ) or instructor consent (quarter system prereq: CS 2300)
Note: None
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Describe the fundamental concepts and application of supervised, unsupervised, semi-supervised, and reinforcement learning
- Compare modern deep neural network architectures, such as dense neural networks, CNNs, encoder-decoders, GANs, and transformers
- Transform raw data into meaningful, informative, and relevant features that contribute to the effectiveness of machine learning algorithms
- Implement deep learning models using popular libraries and frameworks for real-world problems and applications
- Apply agent and state-based frameworks to solve diverse problems, including game playing
- Apply algorithms to identify optimal policies, while considering trade-offs among completeness, optimality, time complexity, and space complexity
- Present results of machine learning and AI projects effectively to both technical and non-technical audiences
Prerequisites by Topic
- Software development
- Object-oriented Python
- Jupyter Notebooks
- Data ingestion, manipulation, and cleaning using DataFrames
- Data exploration and interpretation using visualization and statistical techniques
- Training and applying basic machine learning models
- Ability to apply and interpret appropriate metrics for classification and regression problems
Coordinator
Dr. RJ Nowling
Add to Portfolio (opens a new window)
|
|
Skip to Main Content
