CSC 5610 - AI Tools and Paradigms

• Use a Jupyter notebook environment to write, structure, run, document (with Markdown), and debug Python programs  
• Use Series and Data Frames to load, manipulate, and analyze tabular data sets
• Assess the quality, quantity, and reliability of a data set for a given data science question or task
• Leverage appropriate libraries to solve non-trivial data cleaning, preparation, integration, and transformation challenges 
• Employ a plotting library to visualize data  
• Use exploratory data analysis (e.g., visualizations and statistical tests) to characterize data sets and between pairs of relationships of variables 
• Analyze and describe machine learning algorithms including (but not limited to) logistic regression, SVMs, and Random Forests 
• Explain the geometric and algebraic interpretations of linear and non-linear decision boundaries and their relationship to error metrics (e.g., accuracy)
• Describe implications of feature representations with respect to linear separability 
• Apply approaches for engineering and evaluating new features from existing data 
• Encode non-numerical variables in tabular data as numerical features 
• Use a library to train (apply) machine learning models for (to) classification and regression problems
• Choose and justify appropriate experimental designs (e.g., test-train split, cross-fold validation) for evaluating predictions
• Apply and interpret appropriate metrics for evaluating predictions
• Communicate computational results and interpretations in language appropriate for a technical audience 
• Identify ethical implications of potential biases in data sets, algorithms, and applications of AI

• Recent programming experience with a modern, object-oriented language
• Vectors and matrices