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