Methods and Tools in AI

  1. Expert Systems Definition
  2. Components of Expert Systems
  3. Examples for Expert Systems

  1. Learning from Examples
  2. Types of Learning
    1. Supervised Learning
    2. Unsupervised Learning
    3. Reinforcement Learning
  3. Practical Tools for AI/ML
    1. Programming Environment
    2. Graphical Tools, KNIME
  4. Regression
    1. Univariate Regression
    2. Multivariate Regression
    3. Quality of Regression Models
  5. Classification
    1. Logistic Regression
    2. Decision Trees
    3. Quality of Classifiers
  6. Bias and Variance
  7. Feature Extraction
  8. Ensemble Learning
    1. Random Forest
    2. Regression Trees
    3. Boosted Trees
    4. Hyperparameter-Tuning in ML

  1. Neural Networks
    1. Neurons in Biology
    2. Perceptron
    3. Backpropagation and Gradient Descent
    4. Convolutional Neural Networks (CNN)
    5. Recurrent Neural Networks (RNN)
  2. Deep Learning: Examples and Use Cases
  3. Practical Aspects of Deep Learning
    1. Frameworks
    2. Labelling and Augmentation
    3. Standard Models and Transfer Learning

  1. Examples for NLP Tasks
  2. Text Preprocessing
  3. Embeddings
  4. Transformers
  5. Large Language Models

  1. Programming Environments for AI/ML
    1. Jupyter Notebooks + Python, Anaconda
    2. Frameworks: Scikit-Learn, Tensorflow, PyTorch, Keras etc.
  2. Codeless AI/ML
    1. KNIME Analytics Plattform
    2. Others

Introduction to AI

Applied AI Methodology