Artificial Intelligence for High School Teachers

• basic knowledge of programing in Python

Day 1

• Introduction to artificial intelligence
• What is artificial intelligence, general and narrow AI
• Artificial intelligence subfields (machine learning, state space search, optimization, planning, AI perception, natural language processing)
• Examples of AI applications, history of AI
• Data and information
• Difference between data and information, data sets and their creation, structured and unstructured data, big data and their treatment
• Intuition vs. facts, examples of intuition failures, data-driven decision
• Basics of descriptive statistics, data visualization
• Data representativeness, bias in data, personal data and GDPR

Day 2

• Introduction to machine learning and its applications
• What machine learning is, generalization vs. memorization, the process of machine learning, supervised and unsupervised machine learning
• Classification, regression, clustering, reinforcement learning
• Selected applications of image processing, natural language processing, recommendation and games
• Philosophy of artificial intelligence
• Turing test and Chinese room argument
• Exponential growth, AI safety, fairness and ethics

Day 3

• Basics of data analysis in Python
• Introduction to Python, Jupyter notebooks and Google colab
• Data processing in Pandas, practical examples

Day 4

• Regression
• Definition of regression, the regression process (data training, prediction validation), data structures
  Linear regression, decision trees for regression, practical examples in ScikitLearn
• Classification
• Definition of classification, the classification process (data training, prediction validation)
• Logistic regression, cross entropy, decision trees for classification, examples in ScikitLearn

Day 5

• Artificial neural networks
• Perceptron and its relationship to logistic regression, activation functions, loss functions
• Feed forward neural networks, the back propagation algorithm
• Convolutional neural networks, visualization of neural networks
• Basics of Tensorflow and Keras for the neural network implementation
• Practical examples of neural network applications

Previous courses

Python I - introduction to programming [PYTHON_INTRO]