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CS 540 ULTIMATE CHEAT SHEET CS 540 ULTIMATE CHEAT SHEET
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PROBABILITY & STATISTICS
Definitions
Core Formulas
Expectation & Variance
Common Distributions
Example Problems
LINEAR ALGEBRA
Definitions
Operations
Norms & Orthogonality
Eigenvalues & Eigenvectors
PCA (Principal Component Analysis)
NLP & LANGUAGE MODELS
Definitions
n-gram Models
Smoothing
Perplexity
Word Representations
RNNs (Recurrent Neural Networks)
Transformers & Attention
K-NEAREST NEIGHBORS (KNN)
Algorithm
Distance Functions
Feature Normalization
Choosing k
Properties
Example
K-MEANS CLUSTERING
Algorithm
Formulas
Properties
Choosing k
Example
LINEAR REGRESSION
Model
Loss Functions
Solutions
Gradient Descent Variants
Overfitting vs Underfitting
Example
NEURAL NETWORKS
Perceptron
Activation Functions
Why Activation Functions?
XOR Problem
Multi-Layer Perceptron (MLP)
Loss Functions
Backpropagation
Gradient Descent
Example Calculations
DEEP LEARNING
Vanishing Gradient Problem
Gradient Exploding
ResNets (Residual Networks)
Data Augmentation
Overfitting Solutions
Train/Test Split
CNNs (Convolutional Neural Networks)
Convolution Operation
Output Size Formula
Parameter Counting
Pooling Layers
CNN vs MLP
Architectures
Common Questions
SEARCH ALGORITHMS
Problem Definition
Uniform Cost Search (UCS)
A* Search
Admissible Heuristics
Dominating Heuristics
A* Trace Example
Local Search
Iterative Deepening (IDS)
GAME THEORY
Normal Form Games
Dominant Strategy
Nash Equilibrium
Key Facts
Minimax Algorithm
Game Tree Size
REINFORCEMENT LEARNING
Key Concepts
Markov Decision Process (MDP)
Discount Factor γ
Value Function
Bellman Equation
Value Iteration Algorithm
Value Iteration Example
Q-Function
Q-Learning
ε-Greedy Exploration
Terminal States
Credit Assignment Problem
QUICK REFERENCE FORMULAS
CNNs
Search
RL
Neural Networks
Other
COMMON EXAM TRICKS & PITFALLS
CNN
Search
Game Theory
RL
Neural Networks
Overfitting
K-Means
AI ETHICS & FAIRNESS
Bias in ML
Fairness Definitions
Adversarial Examples
EXCLUDED TOPICS (DON’T STUDY)