AI 104 builds upon introductory courses to provide a more in-depth examination of artificial intelligence concepts and techniques. This article overviews key topics covered in a typical intermediate AI course.

## Prerequisites

AI 104 assumes foundational knowledge from courses like:

• AI 101/103 – Intro to AI concepts
• Data Structures and Algorithms
• Calculus and Linear Algebra
• Probability and Statistics
• Programming experience

These form a base of skills to apply AI methods.

## Mathematical Foundations

AI relies heavily on mathematical concepts including:

### Linear Algebra

• Vector spaces
• Matrices
• Eigendecomposition

### Probability

• Distributions
• Density functions
• Bayes’ theorem

### Calculus

• Differential and integral calculus

### Information Theory

• Entropy
• Mutual information
• Encoding schemes

Practice applying these mathematically is key.

## Core Machine Learning Algorithms

Building on introductory ML, key intermediate techniques include:

### Supervised Learning

• Linear regression
• Logistic regression
• Neural networks
• Support vector machines
• Decision trees and random forests
• K-nearest neighbors

### Unsupervised Learning

• Clustering algorithms like k-means
• Dimensionality reduction techniques
• Association rule learning

### Ensemble Models

• Bagging
• Boosting
• Model blending

## Statistical Learning Theory

Statistical learning provides a framework for analyzing ML algorithms:

• Training versus testing error
• Overfitting and underfitting
• Cross-validation
• Regularization methods
• Performance metrics like precision and recall

This establishes principles for evaluating and tuning models.

## Neural Networks

Neural networks are a powerful ML technique covered extensively:

### Multilayer Perceptrons

• Neural units, activation functions, backpropagation

### Convolutional Neural Networks

• Feature extraction, convolutions, pooling, CNN architectures

### Sequence Models

• Recurrent neural networks, LSTMs, GRUs

### Optimization

Hands-on neural network projects are a key part of the curriculum.

## Unsupervised Learning

Unsupervised learning discovers patterns without labeled training examples:

### Clustering

• K-means, hierarchical, density-based algorithms

### Dimensionality Reduction

• Principal component analysis, singular value decomposition

### Association Rules

• Frequent pattern mining, market basket analysis

Applications like customer segmentation are examined.

## Computer Vision

Computer vision applies ML to analyze visual data:

• Image classification, object detection, image segmentation
• Face detection and recognition
• Pose estimation, image captioning
• Convolutional neural networks for computer vision tasks

Case studies like autonomous driving may be explored.

## Natural Language Processing

Students implement NLP models like:

• Text preprocessing and feature extraction
• Sentiment analysis on textual data
• Document classification and clustering
• Sequence models for language generation and translation
• Topic modeling algorithms
• Speech recognition fundamentals

## Reinforcement Learning

Reinforcement learning develops agents that maximize rewards:

• Multi-armed bandits
• Markov decision processes
• Dynamic programming techniques
• Model-free methods like Q-learning

Game-playing agents and robotics use cases provide concrete examples.