|E-Learning||Course in Ilias|
|Credits||4 SWS / 6 ECTS|
- First lesson in WS 21-22: 11.10.2021
Maybe you know Artificial Intelligence (AI) from Sci-Fi movies? With intelligent machines that are far superior to humans?
If you expect something like this, you might be on the wrong path. First because AI is not only fictitious and future stuff. It is already pervasive in our everyday life. AI is at the heart of weather forecasts, digital assistents (Amazon Echo, Siri, Google Now), recommendation systems, web-search, fraud detection, face recognition, medical- and technical diagnosis, computer games and much more. Even though we have lots of intelligent machines and applications and AI is supposed to bring major shifts in society, you should not be afraid: AI will not reach the full breadth of human intelligence soon. Intuition, emotions, common sense and social skills are only a few factors of intelligence, which make humans superior.
The message is: AI is something practical. It builds rational agents and it provides a toolbox, which enables new and improves existing applications. This lecture provides an insight in this AI toolbox. The elements of this toolbox are algorithms and datastructures. The mathematical and technical features of these algorithms and their application fields are in the focus of this lecture. On an abstract field AI, as covered in this lecture, can be partitioned into the 4 areas depicted in the figure below. For each area the algorithms and concepts, studied in this lecture, are listed.
Structure, Contents, Documents
Gitlab Repo containing all resources of this lecture. Pull repo before each lecture for staying up to date.
|1. Introduction||What is AI? Applications; Categorization; Definitions;|
|1a. Rational Agents||Specificiation of Environment, Types of Rational Agents|
|2. Search and Planning||Heuristic and Non-Heuristic Search and Planning; Planning in Multiplayer Games; Monte Carlo Tree Search; Genetic Algorithm||Python Implementation of Search Algorithms; Solving 8-puzzle (.ipynb); (.html)|
|3. Knowledge Representation and Inference||Knowledge, Inference, Knowledge-Graphs|
|4. Probabilistic Inference||Probability Theory, Probabilistic Inference, Bayes Nets||Bayes Net Implementation (pyAgrum) (.ipynb)|
|5. Machine Learning Intro||Definition, Application, Categorization, Evaluation||Intro Supervised Learning - Regression, Intro Supervised Learning - Classification,Video Machine Learning Intro|
|6. Decision Trees / Ensembles||Characteristics, Entropie, Information Gain, Learning, Boosting and Bagging, Random Forests||Decision Tree Learning, Ensemble Learning|
|7. Neural Networks 1||General concepts, SLPs and MLPs for classification and regression, Stochastic Gradient Descent, Backpropagation Learning,||SLP Notebook, MLP Notebook,Video SLP, MLP|
|8. Unsupervised Learning||Preprocessing, Similarity Measures, k-means, Hierarchical Clustering, DBSCAN|
|9. Deep Neural Networks||Deep Neural Networks in general, Convolutional Neural Networks, Layer-Types, Architectures||CNN Notebook,Video Deep Learning,Convolution Filtering video|
|10. Recurrent Neural Networks||RNN, LSTM, GRU||RNN, LSTM, GRU|
Exercises and further materials
|Exercise 1||Search, Plan, Optimize|
|Exercise 2||Probability, Uncertainty, Bayes Net|
|Exercise 3||Entropy, Information Gain, Decision Trees|
|Exercise 4||Single Layer Perceptron (SLP)|