COSC 410A/LA: Midterm Exams

Here are a list of potential topics for each exam. You should make sure read the textbook, review slides/notes/handouts, and take advantage of the other resources that are available to you on the website and elsewhere.

Midterm Exam 1

• For a problem, identify
  • What type of learning you would apply (supervised vs. unsupervised)?
  • What model is appropriate (linear regression, softmax regression, logistic regression, SVMs, decision trees, k-nearest neighbors, k-means clustering) and why?
  • What type of evaluation makes sense (MAE, MSE, accuracy, precision, recall, f1) and why?
• Identify a good cost function
• Apply a model to determine an output
• Calculate the cost associated with a prediction
• Describe gradient descent with reference to the concepts on a figure (including local vs. global minima)
• The linear regression formula and the trade-offs of closed-form vs. gradient descent optimization
• Different treatments of data with gradient descent (batched, mini-batch, and stochastic) and their trade-offs
• Calculate precision, recall, and f1
• Describe, and graphically demonstrate, k-nearest neighbors
• Describe, and graphically demonstrate, k-means clustering
• Apply the CART algorithm
• Describe the application of the ML pipeline to a problem
• K-fold validation

Midterm Exam 2

• For a problem, identify
  • What type of neural network is appropriate (RNN, CNN, Transformer) and why?
  • What type of architecture is needed (encoder, decoder, encoder-decoder)?
  • What evaluation metric makes sense and why?
• Apply neural network mechanisms, recurrence, kernel, pooling, and attention mechanism, to a concrete example
• Describe different approaches to recommender systems, their strengths/weaknesses, and good uses cases for each
• Calculate recommendation from a recommendation system for a small example
• Describe different autoencoder architectures and their purpose
• Concrete apply q-value iteration and q-learning to small examples
• Build a MDP for a problem description
• Describe hyperparameter tuning
• Understand a q-value matrix
• Use convolution to calculate a specified operation