CS 6340 - Machine Learning

• Understand the basic process of machine learning
• Understand the concepts of learning theory, i.e., what is learnable, bias, variance, overfitting
• Understand the concepts and application of supervised and unsupervised learning
• Analyze and implement basic machine learning algorithms
• Understand the role of optimization in machine learning
• Assess the quality of predictions and inferences
• Apply methods to real-world data sets

• The basic process of machine learning 
• Differences between supervised and unsupervised learning approaches
• Analyze and implement a variety of machine learning algorithms (e.g., k-nearest neighbors, logistic regression, SVMs, decision trees) 
• Confidence in applying, manipulating, and interpreting linear algebra and calculus concepts within the context of machine learning 
• The concepts of learning theory, i.e., what is learnable, bias, variance, overfitting, curse of dimensionality, splitting data for evaluation of model predictions 
• Estimating and plotting decision boundaries described by trained models 
• The role of optimization in machine learning
• Visualizing, applying, and interpreting metric, cost, loss functions, and loss function derivatives to assess the quality of model predictions and fit to data 
• Interpreting the effect of and choose appropriate values for hyper-parameters
• Application of machine learning methods with real-world datasets