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
  • Gradient descent optimization

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:

  • Bias-variance tradeoff
  • 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:

See also  how to delete snapchat ai

Multilayer Perceptrons

  • Neural units, activation functions, backpropagation

Convolutional Neural Networks

  • Feature extraction, convolutions, pooling, CNN architectures

Sequence Models

  • Recurrent neural networks, LSTMs, GRUs

Optimization

  • Gradient descent, adaptive learning rates, batch normalization, dropout

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
  • Exploration/exploitation tradeoff

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

Looking Ahead

By the end of an intermediate AI course, students have built expertise in:

  • Implementing core machine learning algorithms
  • Applying statistical foundations for learning
  • Building and training neural networks
  • Developing computer vision and NLP pipelines
  • Understanding unsupervised learning techniques

This provides strong foundations for specializing in AI application domains or pursuing advanced study in areas like deep learning, robotics, and data science.

The rapid pace of AI research guarantees emerging techniques to explore after an intermediate course. But mastering these fundamentals will provide learners the tools and context to stay up-to-date as AI continues transforming our world.