Lecture Videos

Instructor: Prof. Christian Bizer
Recorded: FSS2020

1. Video: Introduction to Data Mining
2. Video: Cluster Analysis
3. Video: Classification – Part 1
4. Video: Classification – Part 2
5. Video: Classification – Part 3
6. Video: Regression
7. Video: Association Analysis
8. Video: Text Mining

Instructor: Prof. Heiko Paulheim
Recorded: HWS2020

1. Video: Introduction to Data Mining
2. Video: Cluster Analysis
3. Video: Classification – Part 1
4. Video: Classification – Part 2
5. Video: Regression
6. Video: Text Mining
7. Video: Association Analysis

Python

Instructor: Ralph Peeters
Recorded: FSS2022

The exercise sheets and the data sets for the exercise are available on the course page.

1. Screen cast: Simple Preprocessing and Visualization (Intro | Solution)
2. Screen cast: Clustering (Intro | Solution)
3. Screen cast: Classification I (Intro | Solution)
4. Screen cast: Classification II (Intro | Solution)
5. Screen cast: Classification III (Intro | Solution)
6. Screen cast: Regression (Intro | Solution)
7. Screen cast: Text Mining (Intro | Solution)
8. Screen cast: Association Analysis (Intro​​​​​​​ | Solution​​​​​​​​​​​​​​)

RapidMiner

Instructor: Robert Meusel
Recorded: FSS2015

The exercise sheets and the data sets for the exercise are available on the course page.

1. Screen cast: Introduction to RapidMiner
2. Screen cast: Data Preprocessing
3. Screen cast: Exercise 1 – Exploring the Students Dataset
4. Screen cast: Exercise 2 – Customer Segmentation
5. Screen cast: Classification With RapidMiner
6. Screen cast: Optimization With RapidMiner
7. Screen cast: Exercise 3 – Credit Assignment
8. Screen cast: Exercise 4 – Shopping Basket Analysis
9. Screen cast: Text Mining With RapidMiner
10. Screen cast: Exercise 5 – News Article Clustering

Instructor: Prof. Heiko Paulheim
Recorded: FSS2015

1. Video: Data Preprocessing
2. Video: Regression
3. Video: Anomaly Detection
4. Video: Ensembles
5. Video: Time Series Analysis
6. Video: Online Learning (by Robert Meusel)
7. Video: Optimization and Parameter Tuning

Instructor: Prof. Dr. Rainer Gemulla
Slides (all lectures): download here.  (PDF)
Recordings (HWS2025):  download here.

1. Introduction
  1. What is machine learning?
  2. Types of machine learning
2. Basic Concepts
  1. Probability refresher 
  2. Generative and discriminative models 
  3. Model selection
3. Inference and Decision
  1. Estimators
  2. Parameter estimation 
  3. Bayesian inference
  4. Decision
4. Generative Models for Discrete Data
  1. The Beta-Binomial Model
  2. The Dirichlet-Multinomial model
  3. Naive Bayes
5. Classifiers for Continuous data
  1. Logistic regression
  2. Softmax regression
  3. Gaussian Naive Bayes
6. Point Estimation 
  1. MLE & ERM
  2. Gradient-Based Optimizaiton
  3. MAP & RRM
7. Dimensionality Reduction
  1. Encoders, decoders, and matrix decompositions
  2. SVD
  3. Interpreting the SVD
  4. SVD in practice
  5. Latent linear models
8. EM Algorithm & Mixture models
  1. Introduction
  2. The EM Algorithm
  3. Mixture models
9. Kernels and Vector Machines 
  1. Kernels
  2. Kernel machines and vector machines
  3. Kernel trick
  4. Sparse vector machines
10. HPO
   1. HPO problem
   2. Blackbox optimization
   3. Multi-fidelity optimization
   4. HPO in practice

A. Probability refresher
B. Vectors & Matrices

Instructor:  Prof. Dr. Rainer Gemulla
Recorded:  FSS2025
Slides (for all lectures):  download here (PDF)
Recordings:  see here

1. Introduction
2. Feedforward neural networks
   2.1 Embeddings
   2.2 Feedforward neural networks
   2.3 Linear layers
   2.4 Non-Linear layers
   2.5 Multi-Layer perceptrons
3. Gradient-based training
   3.1 Backpropagation
   3.2 Optimizers
   3.3 Architecture design
   3.4 Initialization
4. Layers for categorical data
   4.1 Embedding and softmax layers
   4.2 Word vectors (example)
   4.3 Softmax with many classes
5. Part embeddings
6. Convolutional neural networks
   6.1 Introduction
   6.2 Convolution and cross-correlation
   6.3 Convolutional layers
   6.4 1 × 1 convolutions
   6.5 Pooling
   6.6 CNNs
7. Recurrent neural networks and structured state space models
   7.1 Sequence models
   7.2 RNN encoders
   7.3 Catastrophic forgetting
   7.4 Deep autoregressive models
   7.5 RNN decoders
   7.6 Linear recurrences and SSMs
8. Attention and transformers
   8.1 Attention
   8.2 Transformer encoders
   8.3 From sets to sequences
   8.4 Transformer decoders
9. Graph learning
   9.1 Spectral embeddings
   9.2 Deep learning for graphs
10. Training techniques
    10.1 Data augmentation
    10.2 Pretraining and fine-tuning
    10.3 Prompting

Instructor:  Prof. Dr. Rainer Gemulla
Recorded:  HWS2024
Slides (for all lectures):  download here (PDF)
Recordings:  see here

1. Parallel Distributed Database-Systems (no lecture recordings available)

   1. Parallel and Distributed Database Systems

   2. Parallel Architectures

   3. Vertical and Horizontal Scaling

2. Parallel Database Design

   1. Fragmentation

   2. Allocation

   3. Transparency

3. Data Localization

4. Query Execution

   1. Parallel Execution

   2. Parallel Sort

   3. Parallel Joins

   4. Parallel Aggregation

   5. Parallel Group-By / Projection

   6. Distributed Optimization and Execution

5. MapReduce

   1. Background
   2. MapReduce Programming Model
   3. Hadoop
   4. Distributed Storage
   5. MapReduce Runtime
   6. SQL in MapReduce
   7. Discussion
6. Spark, Dataflows, and Streaming
   1. Beyond MapReduce
   2. Introduction to Spark
   3. Spark Runtime
   4. Streaming
7. Distributed Transactions
   1. Overview
   2. Atomicity
   3. Isolation
   4. Replication
8. NoSQL
   1. Overview
   2. CAP Theorem
   3. Eventual Consistency
   4. Consistent Hashing
   5. Epidemic Protocols

Instructor: Prof. Christian Bizer
Recorded: HWS2019

1. Video: Introduction to Web Data Integration
2. Video: Types of Structured Data on the Web
3. Video: Data Exchange Formats – Part I
4. Video: Data Exchange Formats – Part II
5. Video: Schema Mapping and Data Translation – Part I
6. Video: Schema Mapping and Data Translation – Part II
7. Video: Identity Resolution – Part I
8. Video: Identity Resolution – Part II
9. Video: Data Quality Assessment and Data Fusion – Part I
10. Video: Data Quality Assessment and Data Fusion – Part II

Instructor: Anna Primpeli
Recorded: HWS2019

MapForce can be downloaded from the Altova website including a 30-day test licence. The Java Framework which is used for Identity Resolution and Data Fusion can be downloaded here.

1. Tutorial and screen cast: Introduction to MapForce
2. Tutorial and screen cast: Introduction to Java Identity Resolution Framework
3. Screen cast: Introduction to Java Data Fusion Framework

Instructor: Prof. Simone Ponzetto
Recorded: HWS2015

1. Video: Introduction to Natural Language Processing
2. Video: Linguistics Essentials and Statistics Fundamentals for NLP
3. Video: Regular Expressions and Automata
4. Video: Words and Transducers
5. Video: Collocations
6. Video: N-Gram Language Models Part I
7. Video: N-Gram Language Models Part II
8. Video: POS Tagging Part I
9. Video: POS Tagging Part II
10. Video: Word Sense Disambiguation
11. Video: Information Extraction
12. Video: Machine Translation
13. Video: Vector Semantics (Sparse)
14. Video: Deep learning for NLP 

Instructor:Prof. Dr. Christian Bizer & Prof. Dr. Simone Ponzetto
Recorded: FSS2022

1. Video: Introduction and Course Outline
2. Video: Web Usage Mining – Part I
3. Video: Web Usage Mining – Part II
4. Video: Web Structure Mining – Part I
5. Video: Web Structure Mining – Part II
6. Video: Web Content Mining – Part I
7. Video: Web Content Mining – Part II
8. Video: Web Content Mining – Part III

Instructor: Dr. Laura Dietz
Recorded: FSS2016

1. Video: Boolean Retrieval
2. Video: Evaluation
3. Video: Term Vocabulary and Postings Lists
4. Video: Dictionaries and Tolerant Retrieval
5. Video: Scoring and Results Assembly Part I
6. Video: Scoring and Results Assembly Part II
7. Video: Query Expansion
8. Video: Probabilistic IR, BIM, BM25
9. Video: Language Models for IR
10. Video: Web Crawling
11. Video: Link Analysis
12. Video: Learning to Rank

Instructor:Prof. Dr. Heiko Paulheim
Recorded: HWS2016

1. Video: Introduction
2. Video: RDF
3. Video: RDFS
4. Video: Linked Data and Semantic Web Programming
5. Video: SPARQL
6. Video: OWL Part I
7. Video: OWL Part II
8. Video: Ontology Engineering

Instructor: Prof. Dr. Simone Paolo Ponzetto
Recorded: FSS2017

1. Video: Knowledge Management I (Intro and Knowledge)
2. Video: Knowledge Management II (Management)
3. Video: Information Retrieval I (Foundations)
4. Video: Information Retrieval II (Evaluation)
5. Video: Information Retrieval III (Search Engines)
6. Video: Data Mining I (Classification)
7. Video: Data Mining II (Clustering)
8. Video: Data Mining III (Evaluation)
9. Video: Text Mining I (Intro and applications)
10. Video: Text Mining II (Statistical NLP)
11. Video: Text Mining III (Information Extraction)
12. Video: Social Network Analysis I (Intro to SNA)
13. Video: Social Network Analysis II (Graph Theory)
14. Video: KR for Intelligent Applications

Instructor:Prof. Dr. Heiner Stuckenschmidt
Recorded: HWS2017

1. Video: Organization
2. Video: Relational Decision Agents (Part 1, Part 2, Part 3)
3. Video: Logic and Decision Making (Part 1, Part 2, Part 3)
4. Video: Logical Reasoning (Part 1, Part 2)
5. Video: Quantifying Uncertainty (Part 1, Part 2, Part 3, Part 4, Part 5, Part 6)
6. Video: Graphical Models (Part 1,Part 2, Part 3, Part 4)
7. Video: Decision Theory and Decision Making (Part 1, Part 2, Part 3, Part 4,Part 5)
8. Video: Game Theory (Part 1, Part 2, Part 3)

• Praktische Informatik II (DE)
  • Lecturer: Prof. Dr. Rainer Gemulla
  • Slides (FSS25):download here. (PDF)
  • Recordings (FSS25):dowload here.