CSC 2611 - AI Tools

• Leverage basic shell scripting in Unix for file manipulation, program execution, and debugging  
• Use control, loop, and function syntax to develop algorithmic solutions in Python 
• Use Python’s set, list, tuples, and dictionary to structure and organize data 
• Measure and estimate the runtime and memory utilization of algorithms and data structures  
• Construct regular expressions to solve pattern matching problems and clean data 
• Use a Jupyter notebook environment to write, structure, run, document, and debug Python programs 
• Use Pandas to load, process, and analyze tabular data sets in Series and DataFrames  
• Describe and utilize NumPy array data structures for various numerical use cases  
• Utilize a plotting library to create simple plots such as histograms, bar or column charts, line plots, and scatter plots including legends, appropriate axis ranges and labels, appropriate font sizes and resolutions 
• Apply supervised learning algorithms for classification and regression tasks 
• Explain how to calculate loss, accuracy, and precision from model predictions and ground truth labels 
• Explain the relationship between the size of data sets and the number of variables that can be inferred in a model both for classical and deep learning models 
• Execute and justify splitting training, testing, and validation data for machine learning methods 
• Describe over-fitting in the context of a machine learning problem  
• Justify tradeoffs between hyperparameters when training  
• Explain the GPU execution model at a high level (e.g., SIMD parallelism) and how this is advantageous for training on large data sets / large models 
• Explain how deep learning architectures and hyper-parameters are important to model accuracy and motivate the need to run multiple experiments to find the best model 

• Programming experience in a high-level language