CS 540 Midterm - Comprehensive Review Cheatsheet

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CS 540 Midterm - Comprehensive Review Cheatsheet

Overview

Priority Rankings

Critical Priority (60% of exam)

High Priority (25% of exam)

Medium Priority (13% of exam)

Lower Priority (2% of exam)

Critical Priority Topics

Lecture 11: Perceptron

Lecture 12: Neural Networks II

Lecture 13: Neural Networks III

Lecture 10: Classification - KNN and Naive Bayes

Lecture 2: Probability

High Priority Topics

Lecture 7: Unsupervised Learning I

Lecture 9: Linear Models & Linear Regression

Lecture 3: Linear Algebra and PCA

Medium Priority Topics

Lecture 5: Natural Language Processing

Lecture 1: Course Overview and Probability

Lecture 6: Machine Learning Overview

Lower Priority Topics

Lecture 4: Logic

Lecture 8: Unsupervised Learning II

Study Strategy Recommendations

Recommended Study Schedule (7 Days Before Exam)

Time Allocation During Study

Key Formulas to Memorize

Probability (Lecture 2)

Neural Networks (Lectures 11-13)

Regression (Lecture 9)

Classification (Lecture 10)

Clustering (Lecture 7)

PCA (Lecture 3)

Practice Resources

By Topic (Priority Order)

Full Practice Exams

Common Pitfalls to Avoid

Computational Mistakes

Conceptual Mistakes

Final Checklist

Computational Skills (Must Practice!)

Conceptual Understanding

Exam Statistics Summary