Students who complete Machine Learning (CSU44061/CS7CS4/CS4404) should be able to (1) Decide when machine learning is an appropriate method to solve a problem (2) Understand how machine learning works. (3) Apply machine-learning frameworks to solve real-world problems, and adjust and extend existing algorithms when necessary. The module content includes

  1. Machine Learning Basics (Application Areas, Challenges, Alternatives to Machine Learning)
  2. Machine Learning in Action (Datasets, Frameworks, Evaluation)
  3. Cross-validation and confidence intervals
  4. Overfitting/underfitting (bias-variance trade-off)
  5. Machine Learning Algorithms
    1. Linear Regression
    2. Logistic Regression
    3. Support Vector Machines
    4. Kernel Methods
    5. k-Means Clustering and Mixture Models for Unsupervised Learning
    6. Neural Networks
    7. Deep Learning Algorithms
  6. Use of gradient descent, and extensions for improved scalability (stochastic gradient descent etc)
  7. Probabilistic interpretations of ML algorithms.
  8. Maximum Likelihood and MAP estimators.
  9. Recommender systems