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CS 487: Applied Machine Learning I (JSON)

Catalog description: An introductory course on practical machine learning. An overview of concepts for both unsupervised and supervised learning. Topics include classification, regression, clustering, and dimension reduction. Classical methods and algorithms such as linear regression, neural networks, support vector machines, and ensemble approaches. Recent techniques such as deep learning. Focused on applying of machine learning techniques in application domains.

Prerequisites: At least a C- in C S 272, MATH 191G; or consent of instructor    (Catalog Link)

Credits: 3 (3)

Coordinator: Tuan Le

Textbook: [Optional] Python Machine Learning (Machine Learning and Deep learning with Python, scikit-learn, and TensorFlow) (2nd Edition), by Sebastian Raschka and Vahid Mirjalili. Packt Publishing Ltd., ISBN: 978-1-78712-593-3; or similar textbooks
    (also: )

BS degree role: selected elective

Course Learning Objectives

  1. Implement and utilize different data processing techniques
  2. Differentiate and assess several dimension reduction techniques
  3. Utilize several classifiers (SVM, Decision tree, k-Nearest Neighbor, and logistic regression) and differentiate their advantages and disadvantages
  4. Explain and demonstrate regression analysis
  5. Describe and illustrate clustering approaches
  6. Apply ensemble learning approaches
  7. Implement several neural network classifiers, including deep learning models

Course Practicum Requirements

  1. Be able to write, debug, and run Python programs
  2. Be able to use Python packages and libraries for scientific computing (e.g., NumPy, SciPy, Pandas), for data mining and visualization (e.g., Scikit-learn, Matplotlib, Seaborn)
  3. Be able to use a library (e.g., TensorFlow or Pytorch) for deep learning

Course Topics

  1. Data preprocess: dealing with missing data, feature scaling
  2. Dimensionality Reduction: PCA, LDA, Kernel PCA
  3. Classification Algorithms: Decision Trees, kNN, SVM, Logistic Regression
  4. Ensemble Learning: Bagging, Boosting, Random Forests
  5. Regression Analysis: Linear Regression, Non-linear Regression
  6. Clustering Approaches: k-means, Hierarchical Clustering
  7. Neural Networks and Deep Learning: Perceptron, Adaptive Linear Neurons, Stochastic Gradient Descent, CNN, RNN

Course Improvement Decisions

(Course improvement decisions or recommendations from past assessments)

  1. none

ABET Outcome Coverage

(Provide Mapping to ABET Student Outcomes)

  1. TBD

Other Notes

(Any important notes or issues to consider)

  1. none