Lecture 14: Midterm Review

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Midterm Information

Bayesian Inference

Terminology

Naïve Bayes

Principal Components Analysis (PCA)

Goal

PCA Procedure

Propositional Logic

Basics

Evaluating Sentences

Language Models (NLP)

Basic Idea

Markov Assumption

Unigram Model (k=0)

Bigram Model (k=1)

n-gram Model

Supervised vs. Unsupervised Learning

Supervised Learning

Unsupervised Learning

Clustering

Agglomerative (Hierarchical) Clustering

K-Means Clustering

Spectral Clustering

Linear Regression

Setup

Notational Trick

Matrix Form

Closed-Form Solution (Normal Equations)

K-Nearest Neighbors

Distance Metrics

Algorithm

Neural Networks

Architecture

Softmax

Loss Functions

Gradient Descent

Minibatch Stochastic Gradient Descent (SGD)

Computational Graphs and Backpropagation

Numerical Stability

Practice Problems

Key Takeaways