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:
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.
