GED Course

Introduction to Artificial Intelligence

• What is Artificial Intelligence and why it matters

• AI goals and the concept of rational agents 

• Narrow AI vs General AI vs Superintelligence

• AI vs Machine Learning vs Deep Learning

• Career opportunities in AI

• Assignment: Research AI applications in various industries

 History and Evolution of AI

• Historical evolution of AI from early rule-based systems to modern AI 

• Key milestones in AI development

• AI winters and breakthroughs

• Contributions of Alan Turing, John McCarthy, and other pioneers

• Assignment: Create a timeline of AI history

Philosophy and Foundations of AI

• Philosophical foundations of AI

• The Turing Test and its implications

• The Chinese Room argument

• Can machines think? Classical debates

• Assignment: Write a reflection paper on AI philosophy

Applications of Artificial Intelligence

• Common AI application domains 

• AI in healthcare, finance, education, and retail

• Computer vision, NLP, and recommendation systems 

• Generative AI and creative applications

• Assignment: Analyze an AI application case study

The AI Development Lifecycle

• From problem identification to deployment

• Data collection and preprocessing

• Model selection and training

• Evaluation and iteration

• Assignment: Map the AI lifecycle for a use case

Intelligent Agents

• What are intelligent agents and their role in AI 

• Agent architectures and environments

• Rationality and performance measures

• Types of agents (simple reflex, model-based, goal-based, utility-based)

• Assignment: Design a simple intelligent agent

Agent Environments and Problem Formulation

• Environment types (observable vs unobservable, deterministic vs stochastic)

• Problem formulation for AI solutions 

• Formalizing problems as search spaces

• Assignment: Formulate a real-world problem

Agent Types and Applications

• Software agents vs physical agents

• Autonomous systems and robotics

• Multi-agent systems

• Assignment: Compare different agent types

 Introduction to Search Problems

• Why search is fundamental to AI

• Problem formulation as search 

• Search spaces and state spaces

• Assignment: Identify search problems

Uninformed Search

• Breadth-First Search (BFS) 

• Depth-First Search (DFS)

• Uniform-Cost Search 

• Assignment: Implement BFS and DFS

 Informed Search and Heuristics

• Heuristic search techniques

• A* Search Algorithm 

• Greedy Best-First Search

• Assignment: Implement A* search

 Adversarial Search and Game Playing

• Minimax algorithm 

• Alpha-beta pruning

• Game playing AI (Chess, Go)

• Assignment: Implement a game AI

Local Search and Optimization

• Hill climbing and simulated annealing

• Genetic algorithms and evolutionary computing

• Constraint satisfaction problems 

• Assignment: Apply local search to an optimization problem

Knowledge Representation Fundamentals

• Representing knowledge for intelligent systems

• Propositional and predicate logic 

• Ontologies and semantic networks

• Assignment: Represent knowledge for a domain

Logical Agents and Reasoning

• Logical agents and knowledge bases

• Inference and deduction

• SAT solvers and satisfiability 

• Assignment: Implement logical inference

Planning and Decision Making

• Classical planning representations

• Planning graphs and Graphplan 

• Decision-making under uncertainty 

• Assignment: Create a planning solution

Introduction to Machine Learning

• What is Machine Learning and why it matters

• Types of learning: supervised, unsupervised, reinforcement 

• The machine learning workflow

• Assignment: Research ML applications

Supervised Learning - Part 1

• Regression and classification 

• Linear regression and logistic regression 

• Naive Bayes classifier 

• Assignment: Implement a linear regression model

Supervised Learning - Part 2

• Support Vector Machines (SVM) 

• Decision trees and random forests

• K-Nearest Neighbors (KNN)

• Assignment: Build a classification model

Unsupervised Learning

• Clustering algorithms: K-Means 

• Dimensionality reduction

• PCA and t-SNE

• Assignment: Implement clustering

Reinforcement Learning

• Introduction to reinforcement learning 

• Rewards and state transitions 

• The Bellman equation 

• Q-learning fundamentals 

• Assignment: Train a simple RL agent

Evaluation and Model Selection

• Model evaluation metrics

• Overfitting and underfitting

• Cross-validation

• Assignment: Evaluate and select a model

Introduction to Neural Networks

• The Perceptron: building block of neural networks 

• Activation functions and layers

• How neural networks learn

• Assignment: Build a perceptron

Neural Networks in Depth

• Feedforward networks

• Backpropagation and gradient descent 

• Training neural networks

• Assignment: Train a simple neural network

Convolutional Neural Networks (CNNs)

• Convolutional layers and filters 

• Pooling layers 

• Classical CNN architectures 

• CNN applications in computer vision

• Assignment: Build a CNN for image classification

Recurrent Neural Networks (RNNs) and LSTMs

• Sequence data and RNNs 

• Long Short-Term Memory (LSTM) 

• Applications in NLP and time series

• Assignment: Implement an RNN

Transformer Architecture

• The Transformer architecture 

• Self-attention mechanism 

• Transfer learning and fine-tuning 

• Assignment: Explore a transformer model

Introduction to Generative AI

• What is generative AI and why it matters 

• Overview of foundational models 

• Generative AI use cases and applications

• Assignment: Explore generative AI tools

Social Studies Practice Test

• Full Social Studies section practice exam 

• Review and feedback session

• Assignment: Complete Social Studies practice test

Comprehensive Review - Mathematical Reasoning

• Key concepts review and problem-solving

• Common mistakes and how to avoid them

• Assignment: Targeted math practice

Comprehensive Review - Reasoning Through Language Arts

• Grammar and reading strategies review

• Essay writing best practices 

• Assignment: Targeted RLA practice

Comprehensive Review - Science

• Key science concepts review 

• Experimental design and data interpretation

• Assignment: Targeted Science practice

Comprehensive Review - Social Studies

• Key social studies concepts review 

• Primary source analysis

• Assignment: Targeted Social Studies practice

Full-Length Practice Exam - Part 1

• Mathematical Reasoning practice test

• Reasoning Through Language Arts practice test

• Timed conditions simulation

• Assignment: Complete Part 1 of practice exam

Full-Length Practice Exam - Part 2

• Science practice test

• Social Studies practice test

• Timed conditions simulation

• Assignment: Complete Part 2 of practice exam

Final Assessment & Certification

• Comprehensive review of all subject areas

• Individual progress assessment

• Test day preparation and strategies

• Certification