Please enable JavaScript to use CodeHS

Points

Activity Points
What is AI?
1.1 What is Artificial Intelligence?
1.1.1 What is Artificial Intelligence? 5
1.1.2 What is Artificial Intelligence? 5
1.1.3 A Day in the Life of AI 5
1.1.4 A Day in the Life of AI 5
1.1.5 Guess the Drawing 5
1.1.6 Drawing with AI 5
1.1.7 Drawing with AI 5
1.2 Subsets of Artificial Intelligence
1.2.1 Subsets of Artificial Intelligence 5
1.2.2 Subsets of Artificial Intelligence 5
1.2.3 Using a Natural Language Processor 5
1.2.4 Using a Natural Language Processor 5
1.2.5 Teachable Machine Exploration 5
1.2.6 Teachable Machine Exploration 5
1.2.7 Neural Network Playground 5
1.3 The Ethics of Artificial Intelligence
1.3.1 The Ethics of Artificial Intelligence 5
1.3.2 The Ethics of Artificial Intelligence 5
1.3.3 Bias in Facial Recognition Software 5
1.3.4 Bias in Facial Recognition Software 5
1.3.5 Testing a Biased Dataset 5
1.3.6 Debating the Ethics of Self Driving Cars 5
1.4 Project: Research an Ethical Dilemma in AI
1.4.1 Choosing An Ethical Dilemma 5
1.4.2 Researching Your Ethical Dilemma 5
1.4.3 Creating Your Ethical Presentation 5
1.4.4 Submit Your Ethical Presentation 5
AI in Gaming
2.1 Artificial Intelligence in Gaming
2.1.1 AI in Gaming 5
2.1.2 AI in Gaming 5
2.1.3 Determine Finite States 5
2.1.4 Determine Finite States 5
2.1.5 The Future of AI in Gaming 5
2.1.6 The Future of AI in Gaming 5
2.2 Building Tic Tac Toe
2.2.1 Building Tic Tac Toe 5
2.2.2 Build the Board 5
2.2.3 Take Turn 5
2.2.4 Check Win 5
2.2.5 Complete the Game 5
2.3 Creating a Non Player Character
2.3.1 Pac-Man NPC Exploration 5
2.3.2 Pac-Man NPC Reflection 5
2.3.3 Building a Non Player Character for Tic Tac Toe 5
2.3.4 Tic Tac Toe with Random NPC 5
2.3.5 Random NPC Reflection 5
2.4 Recursion
2.4.1 Search Trees and Recursion 5
2.4.2 Search Trees and Recursion 5
2.4.3 Summing with Recursion 5
2.4.4 Factorials with Recursion 5
2.4.5 Countdown! 5
2.4.6 Reverse a String 5
2.4.7 Bacteria Cultures 5
2.4.8 Exploring Recursion and Tic Tac Toe 5
2.5 Minimax
2.5.1 Creating a Search Tree Algorithm 5
2.5.2 Tic Tac Toe with Minimax 5
2.5.3 Implement Minimax Base Case 5
2.5.4 Implementing Minimax Recursive Case 5
2.5.5 Tracing Our Program 5
2.5.6 Tracing Our Program Reflection 5
2.5.7 Getting the Row Col Values 5
2.5.8 Complete Game with Minimax 5
2.5.9 Tic Tac Toe with Minimax Reflection 5
2.6 Exploring Depth and Pruning
2.6.1 Exploring Depth 5
2.6.2 Adding Depth to Minimax 5
2.6.3 Evaluating Depth! 5
2.6.4 Alpha Beta Pruning 5
2.6.5 Alpha Beta Pruning Speed 5
2.6.6 Alpha Beta Pruning Speed 5
2.6.7 Adding Alpha Beta Pruning 5
2.7 Implementing Connect Four
2.7.1 Building Connect Four 5
2.7.2 Connect Four 5
2.7.3 Challenge: Create Your Own Game 5
AI and Chatbots
3.1 Using Chatbots
3.1.1 What is a Chatbot? 5
3.1.2 What is a Chatbot? 5
3.1.3 Turing Test and the Chatbot 5
3.1.4 Turing Test and the Chatbot Free Response 5
3.1.5 AI Dungeon 5
3.1.6 AI Dungeon Free Response 5
3.2 Building a Rule Based Chatbot
3.2.1 Building a Rule Based Chatbot 5
3.2.2 Building a Rule Based Chatbot Free Response 5
3.2.3 Create Menu + Options 5
3.2.4 Complete your Rule Based Chatbot 5
3.3 Building a Pattern Matching Chatbot
3.3.1 Building a Pattern Matching Chatbot 5
3.3.2 Building a Pattern-Matching Chatbot Free Response 5
3.3.3 Process Requests 5
3.3.4 Creating a Decision Tree 5
3.3.5 Creating a Decision Tree 5
3.3.6 Respond to Requests 5
3.3.7 Chatbot User Testing 5
3.4 Analyzing User Sentiment
3.4.1 File I/O 5
3.4.2 File I/O 5
3.4.3 Accessing a File 5
3.4.4 Using Split to Access Words 5
3.4.5 Creating a Sentiment Dictionary 5
3.4.6 Getting Overall Sentiment 5
3.4.7 Adding User Sentiment to Chatbot 5
3.5 Creating an AI Chatbot
3.5.1 Creating an AI Chatbot 5
3.5.2 Creating an AI Chatbot 5
3.5.3 A Simple Chatbot 5
3.5.4 Your First AI Chatbot 5
3.5.5 Chatbot with Chatterbox Trainers 5
3.5.6 Your First AI Chatbot with CorpusTrainer 5
3.5.7 Teaching your AI Chatbot 5
3.6 Project: Informational Chatbot
3.6.1 Project Requirements 5
3.6.2 Project Brainstorm 5
3.6.3 Build Your Distinct Conversations 5
3.6.4 Build Your Informational Chatbot 5
3.6.5 User Testing Questionnaire 5
3.6.6 User Testing Analysis 5
3.6.7 Fine Tune Your Informational Chatbot 5
Creating Predictive Models
4.1 Introduction to Predictive Models
4.1.1 Introduction to Predictive Models 5
4.1.2 Introduction to Predictive Models 5
4.1.3 Making a Line of Best Fit 5
4.1.4 Making a Line of Best Fit Response 5
4.1.5 Comparing Lines of Best Fit 5
4.1.6 Comparing Lines of Best Fit Response 5
4.2 Correlation
4.2.1 Correlation 5
4.2.2 Correlation 5
4.2.3 Pirates vs Global Warming 5
4.2.4 Spurious Correlations 5
4.2.5 Create Your Own Spurious Correlation 5
4.2.6 Create Your Own Spurious Correlation: Reflection 5
4.3 Programming Linear Regression
4.3.1 Programming Linear Regression 5
4.3.2 Live Coding Demo: Linear Regression 5
4.3.3 Programming Linear Regression 5
4.3.4 Checking Correlation 5
4.3.5 Crickets and Temperature 5
4.3.6 Blood Pressure and Age: Determine Correlation 5
4.3.7 Determine Correlation Response 5
4.3.8 Blood Pressure and Age: Create the Model 5
4.3.9 Blood Pressure and Age: Analyze the Data 5
4.4 Training and Testing Data
4.4.1 Training and Testing Data 5
4.4.2 Live Coding Demo: Training and Testing Data 5
4.4.3 Training and Testing Data 5
4.4.4 Crickets Chirping and Temperature 5
4.4.5 Crickets Chirping Response 5
4.4.6 Blood Pressure and Age: Creating the Model (Duplicate) 5
4.4.7 Blood Pressure and Age: Analyze the Data 5
4.5 Multivariable Linear Regression
4.5.1 Multivariable Linear Regression 5
4.5.2 Live Coding Demo: Multivariable Linear Regression 5
4.5.3 Multivariable Linear Regression 5
4.5.4 Visualizing the Antelope Data 5
4.5.5 Antelope Linear Regression Example 5
4.5.6 Analyzing Independent Variables for Car Prices 5
4.5.7 Analyzing Independent Variables for Car Prices 5
4.5.8 Predicting Car Prices 5
4.5.9 Predicting Car Prices Analysis 5
4.5.10 Gradient Descent 5
4.5.11 Exploring Gradient Descent 5
4.5.12 Exploring Gradient Descent Reflection 5
4.6 Classification and Logistic Regression
4.6.1 Logistic Regression and Classification 5
4.6.2 Live Coding Demo: Logistic Regression 5
4.6.3 Logistic Regression and Classification 5
4.6.4 Iris Classification Example 5
4.6.5 Purchase an SUV 5
4.6.6 Purchase an SUV Reflection 5
4.7 Building Unsupervised Models
4.7.1 Building Unsupervised Models 5
4.7.2 Live Coding Demo: Kmeans and Clustering 5
4.7.3 Building Unsupervised Models 5
4.7.4 Clustering and Old Faithful 5
4.7.5 Kmeans and Complex Shapes 5
4.7.6 Customer Segmentation 5
4.7.7 Customer Segmentation Analysis 5
4.7.8 Clustering and Image Compression 5
4.7.9 Image Compression Exploration 5
4.7.10 Image Compression Reflection 5
4.8 Creating Your Own Predictive Model
4.8.1 Project: Your Own Predictive Model 5
4.8.2 Project Rubric 5
4.8.3 Step 2: Choosing the Best Algorithm 5
4.8.4 Get to Know You Dataset 5
4.8.5 Step 2: Choose Your Model 5
4.8.6 Step 3: Program Your Model 5
4.8.7 Multivariable Linear Regression Model 5
4.8.8 Logistic Regression Model 5
4.8.9 Clustering (Kmeans) Model 5
4.8.10 Step 4: Analyze and Present! 5
Programs Used as Examples
5.1 Examples for AI in Gaming
5.1.1 Determine Finite States 5
5.1.2 Example Minimax Game 5
5.1.3 Alpha Beta Pruning Speed 5
5.1.4 Alpha Beta Pruning Speed (Duplicate) 5
5.1.5 Connect Four 5
5.1.6 Connect Four with Miniax 5
5.2 Examples for Chatbot
5.2.1 Rule Based Chatbot 5
5.2.2 Pattern Matching Chatbot 5
5.2.3 Sentiment Analyzer 5
5.3 Creating Predictive Models
5.3.1 Teen Pregnancy vs Poverty Example 5
5.3.2 Line of Best Fit 5
5.3.3 Housing Prices and Room Numbers 5
5.3.4 Housing Prices + Line of Best Fit 5
5.3.5 Linear Regression with Visual 5
AI Python Bootcamp
6.1 Tuples
6.1.1 Tuples 1
6.1.2 Tuples 5
6.1.3 A Tuple Is a Sequence 1
6.1.4 A Tuple is Heterogenous 1
6.1.5 Tuples With a Single Element 1
6.1.6 Concatenating Tuples 1
6.1.7 Fix This Tuple 5
6.1.8 Citation 5
6.1.9 Diving Contest 5
6.1.10 Coordinate Pairs 10
6.2 Lists
6.2.1 Lists 1
6.2.2 Lists 5
6.2.3 A List Is Like a Mutable Tuple 1
6.2.4 String <--> List 1
6.2.5 Spell It Out 5
6.2.6 Splitting a String 1
6.2.7 Listed Greeting 5
6.2.8 List of Tuples, Tuples of Lists 1
6.3 For Loops and Lists
6.3.1 For Loops and Lists 1
6.3.2 For Loops and Lists 2
6.3.3 For Loops and Lists 1
6.3.4 For Loops and Lists, Part 2 1
6.3.5 Max In List 5
6.3.6 Owls 5
6.3.7 Exclamat!on Po!nts 5
6.3.8 Word Ladder 10
6.3.9 Owls, Part 2 10
6.4 List Methods
6.4.1 List Methods 1
6.4.2 List Methods 5
6.4.3 append and extend 1
6.4.4 How Many Names? 5
6.4.5 Five Numbers 5
6.4.6 sort 1
6.4.7 Librarian 5
6.4.8 reverse 1
6.4.9 count 1
6.4.10 remove 1
6.4.11 Take a Thing Out, Sort It and Reverse It 5
6.4.12 Librarian, Part 2 10
6.4.13 Lists Badge 1
6.5 2d Lists
6.5.1 2d Lists 1
6.5.2 2d Lists 5
6.5.3 A List of Lists 1
6.5.4 Grid 1
6.5.5 2d Lists and Slices 1
6.5.6 Checkerboard, v1 5
6.5.7 Checkerboard, v2 5
6.5.8 Checkerboard, v3 5
6.5.9 Tic Tac Toe 10
6.6 Dictionaries
6.6.1 Dictionaries 1
6.6.2 Dictionaries 5
6.6.3 Keys and Values 1
6.6.4 The in Keyword 1
6.6.5 Phone Book 5