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