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Advanced Seminar in Intelligent Systems
סמינר מתקדם במערכות נבונות​

202-2-1551 ​Fall 2021-22 סמסטר א

Lecturer: Prof. Moshe Sipper

Wednesday 10-12, Classroom: 90/235

Course Description

• Each student will give a talk on a topic or a paper from the research literature.
• Attendance is mandatory.
• ​Presentation length: 45 minutes. 2 talks each week.
• You may (but are not required to) email me your presentation a few days before your talk (but not the night before!).
• Each student must submit a 1-paragraph summary for each of the 2 weekly talks. 

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 (just to get an idea — choosing your own paper is preferred)

• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
• 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
• ​Random Forests​

Literature, vids, datasets, and other resources

Lectures
John Doe, ...