Moshe Sipper
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Advanced Seminar in Intelligent Systems
סמינר מתקדם במערכות נבונות​

Machine Learning
אתר בבנייה
202-2-1551 ​Spring 2021 סמסטר ב
Lecturer: Prof. Moshe Sipper
Course Description
  • Each student will give a talk on a topic or a paper from the research literature in Machine Learning.
  • Attendance is mandatory.
  • Talks will be given in English.
  • A partial list of papers/topics is given below — you're more than welcome to suggest your own!
Schedule
  • March...
Topics
  • Machine learning in practice: evaluation, dataset splits, cross-validation, performance measures, bias/variance tradeoff
  • Supervised learning: models, features, objectives, model training, overfitting, regularization
  • Linear and logistic regression
  • Bagging
  • Boosting
  • Clustering, k-means
  • Artificial neural networks
  • Deep learning​
  • ML as part of (the) data science (pipeline)
Some papers
  • A Few Useful Things to Know About Machine Learning
  • Classification: Basic Concepts, Decision Trees, and Model Evaluation
  • Decision trees: an overview and their use in medicine
  • Data clustering: 50 years beyond K-means
  • Boosting algorithms: A review of methods, theory, and applications
  • Random Forests​
Literature, vids, datasets, and other resources
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