Provided by Prof. Dr. Johannes Maucher, HdM Institute for Applied Artificial Intelligence
Welcome to this lecture platform on Artificial Intelligence and Machine Learning. The lectures are password protected, please ask your instructor for access credentials.
HdM Lectures
MI-7 Bachelor Lectures
Artificial Intelligence
Introduces into the main concepts and algorithms of the four AI categories: Search & Plan, Knowledge & Inference, Modelling of Uncertainty and Machine Learning.
Natural Language Processing
Introduces conventional and modern techniques which enable computers to understand written natural language: Crawling, text-preprocessing, language models etc. The realisation of NLP applications, such as text-classification, Named Entity Recognition, Question Answering, Translation etc. is discussed.
Selected Topics of AI
This lecture introduces in programming deep neural network applications with PyTorch. First, the basic concepts of PyTorch are introduced. Then sample neural network applications of the domains 1.) structured data, 2.) images and 3.) texts are implemented.
Computer Science Master Lectures
Machine Learning
Introduces the concepts and algorithms of conventional machine learning and deep learning. Modern neural network architectures such as CNNs, Autoencoder, GANs, Diffusion-Models, Transformer, DQN are introduced.
Object Recognition
Introduces the concepts and algorithms of conventional object recognition and Deep Learning based object recognition
Data Science Master Lectures
Python for Data Science
First basic Python concepts like data structures, control structures, functions, classes etc. are introduced. In the second part the most important packages for Data Science, e.g. Numpy, Pandas, Matplotlib, Plotly are applied.
Machine Learning
Implementation of conventional Machine Learning with scikit-learn and of Deep Learning with tensorflow and keras.
Lecture Independent
1. Concepts
Basics of Probability Theory
Basic concepts and notions of Probability Theory and Statistics.
2. Basic Maths for AI and ML
Basic Maths for AI and ML
Basic Maths for Artificial Intelligence and Machine Learning
External Seminars
2. Algorithms
a. ML for structured data
Conventional ML algorithms.
b. ML for image and video
Deep Learning algorthms for vision, e.g. CNNs...
c. ML for sequential data and text
Deep learning for sequences, e.g. RNNs, Transformer, ...
3. Implementation
a. ML for structured data
Implementation with scikit-learn.
b. ML for image and video
Implementation with Tensorflow and Keras.
c. ML for sequential data and text
Implementation with Tensorflow and Keras.
