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Nov 21, 2024
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CS 6230 - AI Tools3 lecture hours 2 lab hours 4 credits
Course Description
This course introduces topics, tools, and methods used in modern AI practice. Subjects of study include data preparation, exploratory data analysis, computational modeling, visualization, machine learning, and deep learning. Labs, discussions, and assignments expose students to modern tools and concepts such as Jupyter Notebooks, Python, NumPy, Pandas, SciKitLearn, MatPlotLib, Keras, functional programming, vectorization, GPU-enabled libraries, and high performance computing. (prereq: enrollment in Machine Learning Graduate Certificate program)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Learn and apply the scientific method to data analysis and inference
- Clean and manage data using functional programming and vectorization with libraries such as Pandas and NumPy
- Structure data for analysis and manipulation
- Manipulate and train machine learning models of real-world problems based on data
- Characterize the accuracy of a trained model and adjust training hyper-parameters to improve accuracy
- Predict the runtime and memory utilization of algorithms based on complexity analysis methods
- Communicate data interpretations including generating meaningful data visualizations
- Leverage high-performance computing methods to efficiently scale large computational problems
Prerequisites by Topic
- Substantial, contemporary programming experience
- Fundamental probability and statistics
- Derivative calculus
Coordinator
Dr. Derek Riley
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