Lectures related to General-purpose modeling

Probabilistic Information Retrieval

Covers Probabilistic Information Retrieval, including Query Likelihood Model, Language Modeling, and smoothing techniques for non-occurring terms.

Probabilistic Information Retrieval

Covers probabilistic information retrieval, query likelihood models, language modeling, and relevance feedback algorithms.

Probabilistic Retrieval Models

Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.

Probabilistic Retrieval: Practical Relevance Feedback

Explores practical relevance feedback in probabilistic retrieval and query optimization.

Biological structure and function emerge from unsupervised learning

Delves into how biological structure and function are decoded through unsupervised learning of protein sequences.

Probabilistic Retrieval

Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.

Integrative Benchmarking: Advancing System Models of Human Intelligence

Explores advancing system models of human intelligence through integrative benchmarking and the importance of Brain-Score for fair model comparisons.

An Introduction to Systems Engineering: Requirements, Architecture, and Concept Generation

Covers the 'V-Model' of Systems Engineering, emphasizing good requirements, system architecture, concept generation, and concept selection.

Pretraining: Transformers & Models

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

Pre-Training: BiLSTM and Transformer

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

System Modeling Languages

Explores the significance of System Modeling Languages like OPM, SysML, and Modelica in modern Systems Engineering.

State Space Models: Expressivity and Transformers

Covers state space models and their expressivity compared to transformers, focusing on attention mechanisms and computational efficiency.

Optimal Decision Making with CVXPY

Introduces CVXPY for optimal decision making through atomic functions and operators.

BERT: Pretraining and Applications

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Contextual Representations: ELMO and BERT Overview

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.