Lecture Videos
The Data and Web Science Group records core lectures for Master students on video and provides screen casts of accompanying exercises in order to enable students to be more flexible in their learning patterns.
Up till now, we have recorded the Data Mining I, Data Mining II, Web Mining, Web Data Integration, Information Retrieval and Web Search, Text Analytics, Large-scale Data Management, Decision Support and Knowledge Mangement lectures and provide screen casts for the Data Mining I and Web Data Integration exercises.
Lots of thanks to the Referat Neue Medien of the Stabsstelle Studium und Lehre for supporting us in recording the lecture videos.
Please note that the videos can only be accessed from within the University of Mannheim. If students want to watch them from home, they need to connect to the university via VPN.
Lecture: Data Mining I
Instructor: Prof. Christian Bizer
Recorded: FSS2020- Video: Introduction to Data Mining
- Video: Cluster Analysis
- Video: Classification – Part 1
- Video: Classification – Part 2
- Video: Classification – Part 3
- Video: Regression
- Video: Association Analysis
- Video: Text Mining
Instructor: Prof. Heiko Paulheim
Recorded: HWS2020- Video: Introduction to Data Mining
- Video: Cluster Analysis
- Video: Classification – Part 1
- Video: Classification – Part 2
- Video: Regression
- Video: Text Mining
- Video: Association Analysis
Tutorials & Exercises: Data Mining I
Python
Instructor: Ralph Peeters
Recorded: FSS2022The exercise sheets and the data sets for the exercise are available on the course page.
- Screen cast: Simple Preprocessing and Visualization (Intro | Solution)
- Screen cast: Clustering (Intro | Solution)
- Screen cast: Classification I (Intro | Solution)
- Screen cast: Classification II (Intro | Solution)
- Screen cast: Classification III (Intro | Solution)
- Screen cast: Regression (Intro | Solution)
- Screen cast: Text Mining (Intro | Solution)
- Screen cast: Association Analysis (Intro | Solution)
RapidMiner
Instructor: Robert Meusel
Recorded: FSS2015The exercise sheets and the data sets for the exercise are available on the course page.
- Screen cast: Introduction to RapidMiner
- Screen cast: Data Preprocessing
- Screen cast: Exercise 1 – Exploring the Students Dataset
- Screen cast: Exercise 2 – Customer Segmentation
- Screen cast: Classification With RapidMiner
- Screen cast: Optimization With RapidMiner
- Screen cast: Exercise 3 – Credit Assignment
- Screen cast: Exercise 4 – Shopping Basket Analysis
- Screen cast: Text Mining With RapidMiner
- Screen cast: Exercise 5 – News Article Clustering
Lecture: Data Mining II
Instructor: Prof. Heiko Paulheim
Recorded: FSS2015- Video: Data Preprocessing
- Video: Regression
- Video: Anomaly Detection
- Video: Ensembles
- Video: Time Series Analysis
- Video: Online Learning (by Robert Meusel)
- Video: Optimization and Parameter Tuning
Lecture: Machine Learning
Instructor: Prof. Dr. Rainer Gemulla
Recorded: HWS2020 (slides from HWS2022 (PDF, 35 MB))1. Introduction
1. What is Machine Learning?
2. Types of Machine Learning
3. Basic Concepts & Summary2. Inference and Decision
1. Probability Refresher
2. Generative & Discriminative Models
3. Parameter Estimation
4. Decision & Summary3. Generative Models for Discrete Data
1. The Beta-Binomial Model
2. The Dirichlet-Multinomial Model
3. Naive Bayes & Summary4. Logistic Regression
1. Logistic Regression
2. Maximum Likelihood Estimation & Empirical Risk Minimization
3. Model Fitting
4. MAP Estimation
5. Softmax Regression & Summary5. Dimensionality Reduction
1. Matrix Decompositions
2. The Singular Value Decomposition
3. Interpreting the SVD
4. Using the SVD
5. Latent Linear Models & Wrap-Up6. The EM Algorithm & Mixture Models
1. Introduction
2. The EM Algorithm
3. Mixture Models & Summary7. Kernels and Vector Machines
1. Kernels
2. Kernel Machines & Vector Machines
3. The Kernel Trick
4. Support Vector Machines & Summary8. Hyperparameter Optimization
1. The Hyperparameter Optimization Problem
2. Blackbox Optimization
3. Multi-Fidelity Optimization
4. HPO in PracticeA. Probability Refresher
B. Vectors and Matrices
1. Vectors
2. Matrices & SummaryLecture: Deep Learning
Instructor: Prof. Dr. Rainer Gemulla
Recorded: FSS2025
Slides (for all lectures): download here (PDF)
Recordings: see here- Introduction
- 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 - Gradient-based training
3.1 Backpropagation
3.2 Optimizers
3.3 Architecture design
3.4 Initialization - Layers for categorical data
4.1 Embedding and softmax layers
4.2 Word vectors (example)
4.3 Softmax with many classes - Part embeddings
- 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 - 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 - Attention and transformers
8.1 Attention
8.2 Transformer encoders
8.3 From sets to sequences
8.4 Transformer decoders - Graph learning
9.1 Spectral embeddings
9.2 Deep learning for graphs - Training techniques
10.1 Data augmentation
10.2 Pretraining and fine-tuning
10.3 Prompting
Lecture: Large-Scale Data Management
Instructor: Prof. Dr. Rainer Gemulla
Recorded: HWS2024
Slides (for all lectures): download here (PDF)
Recordings: see hereParallel Distributed Database-Systems (no lecture recordings available)
Parallel and Distributed Database Systems
Parallel Architectures
Vertical and Horizontal Scaling
Parallel Database Design
Fragmentation
Allocation
Transparency
Data Localization
Query Execution
Parallel Execution
Parallel Sort
Parallel Joins
Parallel Aggregation
Parallel Group-By / Projection
Distributed Optimization and Execution
MapReduce
- Background
- MapReduce Programming Model
- Hadoop
- Distributed Storage
- MapReduce Runtime
- SQL in MapReduce
- Discussion
- Spark, Dataflows, and Streaming
- Beyond MapReduce
- Introduction to Spark
- Spark Runtime
- Streaming
- Distributed Transactions
- Overview
- Atomicity
- Isolation
- Replication
- NoSQL
- Overview
- CAP Theorem
- Eventual Consistency
- Consistent Hashing
- Epidemic Protocols
Lecture: Web Data Integration
Instructor: Prof. Christian Bizer
Recorded: HWS2019- Video: Introduction to Web Data Integration
- Video: Types of Structured Data on the Web
- Video: Data Exchange Formats – Part I
- Video: Data Exchange Formats – Part II
- Video: Schema Mapping and Data Translation – Part I
- Video: Schema Mapping and Data Translation – Part II
- Video: Identity Resolution – Part I
- Video: Identity Resolution – Part II
- Video: Data Quality Assessment and Data Fusion – Part I
- Video: Data Quality Assessment and Data Fusion – Part II
Tutorials: Web Data Integration
Instructor: Anna Primpeli
Recorded: HWS2019MapForce 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.
- Tutorial and screen cast: Introduction to MapForce
- Tutorial and screen cast: Introduction to Java Identity Resolution Framework
- Screen cast: Introduction to Java Data Fusion Framework
Lecture: Text Analytics
Instructor: Prof. Simone Ponzetto
Recorded: HWS2015- Video: Introduction to Natural Language Processing
- Video: Linguistics Essentials and Statistics Fundamentals for NLP
- Video: Regular Expressions and Automata
- Video: Words and Transducers
- Video: Collocations
- Video: N-Gram Language Models Part I
- Video: N-Gram Language Models Part II
- Video: POS Tagging Part I
- Video: POS Tagging Part II
- Video: Word Sense Disambiguation
- Video: Information Extraction
- Video: Machine Translation
- Video: Vector Semantics (Sparse)
- Video: Deep learning for NLP
Lecture: Web Mining
Instructor:Prof. Dr. Christian Bizer & Prof. Dr. Simone Ponzetto
Recorded: FSS2022Web Search and Information Retrieval
Instructor: Dr. Laura Dietz
Recorded: FSS2016- Video: Boolean Retrieval
- Video: Evaluation
- Video: Term Vocabulary and Postings Lists
- Video: Dictionaries and Tolerant Retrieval
- Video: Scoring and Results Assembly Part I
- Video: Scoring and Results Assembly Part II
- Video: Query Expansion
- Video: Probabilistic IR, BIM, BM25
- Video: Language Models for IR
- Video: Web Crawling
- Video: Link Analysis
- Video: Learning to Rank
Lecture: Semantic Web Technologies
Instructor:Prof. Dr. Heiko Paulheim
Recorded: HWS2016- Video: Introduction
- Video: RDF
- Video: RDFS
- Video: Linked Data and Semantic Web Programming
- Video: SPARQL
- Video: OWL Part I
- Video: OWL Part II
- Video: Ontology Engineering
Lecture: Knowledge Management
Instructor: Prof. Dr. Simone Paolo Ponzetto
Recorded: FSS2017- Video: Knowledge Management I (Intro and Knowledge)
- Video: Knowledge Management II (Management)
- Video: Information Retrieval I (Foundations)
- Video: Information Retrieval II (Evaluation)
- Video: Information Retrieval III (Search Engines)
- Video: Data Mining I (Classification)
- Video: Data Mining II (Clustering)
- Video: Data Mining III (Evaluation)
- Video: Text Mining I (Intro and applications)
- Video: Text Mining II (Statistical NLP)
- Video: Text Mining III (Information Extraction)
- Video: Social Network Analysis I (Intro to SNA)
- Video: Social Network Analysis II (Graph Theory)
- Video: KR for Intelligent Applications
Lecture: Decision Support
Instructor:Prof. Dr. Heiner Stuckenschmidt
Recorded: HWS2017- Video: Organization
- Video: Relational Decision Agents (Part 1, Part 2, Part 3)
- Video: Logic and Decision Making (Part 1, Part 2, Part 3)
- Video: Logical Reasoning (Part 1, Part 2)
- Video: Quantifying Uncertainty (Part 1, Part 2, Part 3, Part 4, Part 5, Part 6)
- Video: Graphical Models (Part 1,Part 2, Part 3, Part 4)
- Video: Decision Theory and Decision Making (Part 1, Part 2, Part 3, Part 4,Part 5)
- Video: Game Theory (Part 1, Part 2, Part 3)