Artificial intelligence methods and tools

Artificial intelligence (AI) methods and tools are fundamental to developing and deploying effective AI solutions across various fields. Understanding these methods enables practitioners to create more accurate models, streamline processes, and solve complex problems with greater efficiency. By leveraging the right tools, AI practitioners can enhance the performance and scalability of their systems, making AI more accessible and powerful.

Machine Learning (ML) Techniques

Essential for building predictive models, ML techniques like supervised, unsupervised, and reinforcement learning allow for diverse applications in data analysis and decision-making.

Deep Learning

A subset of ML, deep learning focuses on neural networks that can automatically learn features from large amounts of data, leading to breakthroughs in image recognition, natural language processing, and more.

Natural Language Processing (NLP)

NLP tools enable computers to understand, interpret, and generate human language, playing a crucial role in applications like chatbots, translation services, and sentiment analysis.

AI Development Tools

Environments like Jupyter Notebooks and frameworks like TensorFlow and PyTorch are critical for developing, testing, and deploying AI models, providing the infrastructure needed for modern AI projects.

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
Artificial intelligence methods and tools

Introduction to AI

Applied AI Methodology

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