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CS205: Building with Artificial Intelligence
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Course Introduction
Course Syllabus
Unit 1: What Is Artificial Intelligence?
1.1: The Turing Test
The Turing Test for Intelligence
Why the Turing Test Is Important
1.2: The Four Types of AI
Is Intelligence How You Think or the Output of Thinking?
Unit 1 Assessment
Unit 1 Assessment
Unit 2: Agent-Based Approach to AI
2.1: Introduction to Agent-Based AI
Agents, Agent Types, and Their Capabilities
2.2: Analyzing Environmental Characteristics
Properties of Problem Environments and How to Analyze Them
Unit 2 Assessment
Unit 2 Assessment
Unit 3: Machine Learning and Its Importance
3.1: Learning in AI and Agents
Supervised, Unsupervised, and Reinforcement ML
3.2: Applications of ML in Neural Networks
Newer Machine Learning Models and Applications
Unit 3 Assessment
Unit 3 Assessment
Unit 4: Machine Learning Algorithms
4.1: Classification Algorithms
Classification versus Regression
Importance of Classification and Regression in Machine Learning
Classification Using K-nearest Neighbors Algorithm
4.2: Classification Algorithm Performance
False Positives / False Negatives / Confusion Matrix
Precision and Recall Calculations from Confusion Matrix
Linear Regression – How It Works
4.3: Linear Regression Algorithms
Metrics for Linear Regression Effectiveness: R-squared, MSE and RSE
Lasso and Ridge Regression
Improving Linear Regression by Reducing Residual Errors
4.4: Other Supervised ML Classification Algorithms
Classification Using Decision Trees
Classification Using Logistic Regression
Applying Bayes' Theorem in Machine Learning
4.5: Unsupervised Learning and Reinforcement Learning
Unlabelled Data and Unsupervised Machine Learning
Principles and Applications of Reinforcement Learning
4.6: ML Using Neural Networks
Introduction to Neural Networks Basics
Neural Networks: Types and Applications
Unit 4 Assessment
Unit 4 Assessment
Unit 5: Problem-Solving Methods in AI
5.1: Integrating ML Skills
Applying Classification to Determine Insurability
How Regression Is Applied in Contemporary Computing
Using Neural Networks in Cancer Detection
5.2: General AI Problem-Solver Architecture
Characteristics of General Problem-Solver
5.3: Designing a General Problem-Solving Agent
How GPS Is Used
Computational Tractability of GPS
Unit 5 Assessment
Unit 5 Assessment
Unit 6: Search Algorithms
6.1: Uninformed Search Algorithms
Uninformed or Brute Force Search
Depth First Search Algorithm
Breadth First Search Algorithm
Uniform Cost Search Algorithm
6.2: Heuristic Search Algorithms
Heuristics and Using Them to Improve Search
Overview of A* Search and Analysis of Performance
Unit 6 Assessment
Unit 6 Assessment
Unit 7: Iterative Improvement Algorithms
7.1: Using Iterative Improvement to Solve Problems
Iterative Improvement Algorithms and Hill-Climbing
Constraint Satisfaction Problems and Their Importance
7.2: Improving Algorithm Efficiency
How Simulated Annealing Improves Hill-Climbing
Improving Mediocre Solutions Using Genetic Algorithms
Unit 7 Assessment
Unit 7 Assessment
Unit 8: Game-Playing Models
8.1: Game Trees and the Minimax Algorithm
Principles of Game Trees and How to Create One
Using the Minimax Algorithm in Adversarial Games
Assumptions Underlying Minimax Approach
8.2: Game-Playing Strategies
The Alpha Beta Pruning Algorithm
Tackling Multi-person Games
Unit 8 Assessment
Unit 8 Assessment
Unit 9: Natural Language Processing
9.1: Foundations of NLP
NLP Overview, Challenges, and Applications
9.2: How NLP Is Used
Formal Analysis of Natural Language Structures
Topic Extraction from Long Text
Sentiment Analysis
9.3: NLP Models and Methods
Long Short-Term Memory Models
Statistical Methods in NLP
9.4: Generative AI
What Is Generative AI?
How Generative AI Works
Popular Generative AI Tools
Unit 9 Assessment
Unit 9 Assessment
Unit 10: Reasoning Agents
10.1: Propositional Logic
Propositional Logic
Using PL to Describe Properties of Systems
10.2: First Order Logic
First Order Logic (FOL)
Using FOL to Describe the Properties of Systems
10.3: Bayesian Reasoning and Uncertainty
Conditional Probability
Applying Bayes' Theorem in Deduction
10.4: Modeling Causality with Bayesian Networks
Bayesian Networks
Markov Chains
Applications of Hidden Markov Chains
Unit 10 Assessment
Unit 10 Assessment
Study Guide
Course Feedback Survey
Course Feedback Survey
Certificate Final Exam
CS205: Certificate Final Exam
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CS205: Building with Artificial Intelligence
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