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Course Syllabus

CS205: Building with Artificial Intelligence

Course Introduction

Course Syllabus

Unit 1: What Is Artificial Intelligence?

1.1: The Turing Test

1.2: The Four Types of AI

Unit 1 Assessment

Unit 2: Agent-Based Approach to AI

2.1: Introduction to Agent-Based AI

2.2: Analyzing Environmental Characteristics

Unit 2 Assessment

Unit 3: Machine Learning and Its Importance

3.1: Learning in AI and Agents

3.2: Applications of ML in Neural Networks

Unit 3 Assessment

Unit 4: Machine Learning Algorithms

4.1: Classification Algorithms

4.2: Classification Algorithm Performance

4.3: Linear Regression Algorithms

4.4: Other Supervised ML Classification Algorithms

4.5: Unsupervised Learning and Reinforcement Learning

4.6: ML Using Neural Networks

Unit 4 Assessment

Unit 5: Problem-Solving Methods in AI

5.1: Integrating ML Skills

5.2: General AI Problem-Solver Architecture

5.3: Designing a General Problem-Solving Agent

Unit 5 Assessment

Unit 6: Search Algorithms

6.1: Uninformed Search Algorithms

6.2: Heuristic Search Algorithms

Unit 6 Assessment

Unit 7: Iterative Improvement Algorithms

7.1: Using Iterative Improvement to Solve Problems

7.2: Improving Algorithm Efficiency

Unit 7 Assessment

Unit 8: Game-Playing Models

8.1: Game Trees and the Minimax Algorithm

8.2: Game-Playing Strategies

Unit 8 Assessment

Unit 9: Natural Language Processing

9.1: Foundations of NLP

9.2: How NLP Is Used

9.3: NLP Models and Methods

9.4: Generative AI

Unit 9 Assessment

Unit 10: Reasoning Agents

10.1: Propositional Logic

10.2: First Order Logic

10.3: Bayesian Reasoning and Uncertainty

10.4: Modeling Causality with Bayesian Networks

Unit 10 Assessment

Study Guide

Course Feedback Survey

Certificate Final Exam

Quizzes