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linear-algebra-and-matrix-algebra

linux-basics

lstm_encoder_decoder

Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data

machine-design

machine-learning

This course covers basic concepts in ML, such as exploratory data analysis, dimensionality reduction (PCA), regression and classification, clustering, deep learning and reinforcement learning

machine-learning-advanced-probabilistic-methods

The course covers concepts in probabilistic machine learning: independence, conditional independence, mixture models, EM algorithm, Bayesian networks, latent linear models, and algorithms for exact and approximate inference, with an emphasis on variational inference, which helps derive approximate inference algorithms for complex models

machine-learning-supervised-methods

This course covers ML supervised techniques, such as generalization error analysis and estimation, model selection, optimization and computational complexity, linear models, support vector machines and kernel methods, boosting; feature selection and sparsity, multi-layer perceptrons, multi-class classification and preference learning

machine-learning-with-python

This course covers regression, classification, model validation and selection, clustering and dimensionality reduction. This is a follow-up course from Machine Learning course

material-modelling-in-civil-engineering

This course covers fundamentals of material modelling within the framework of continuum mechanics, such as physical and mathematical description of key features of common material behaviour in civil engineering related to their thermo-mechanical response. Computational tools commonly used in material modelling in civil engineering are also covered

materials-safety

This course covers failure mechanisms (cleavage, fatigue, creep, environmentally assisted degradation) and related research papers. Additionally, it features real life failure cases, and two materials testing laboratory exercises on tensile testing and fracture testing

materials-science-and-engineering

mechanical-testing-of-materials

This course covers measurement of force, displacement, and strain, loadframes, actuators; and grips quasi-static, dynamic, and cyclic loading; selected special challenges in mechanical testing; digital image correlation and other full-field measurement techniques. It also covers introduction inverse problem methodologies in experimental mechanics

methods-of-data-mining

The course covers fundamental data mining problems, such as pattern discovery, graph mining, and clustering different types of data. The main emphasis is in learning the basic principles of data mining and their application in practice, including method selection, validation, and scalablity issues.

modeling-in-applied-mechanics

multiple-choice-app

An online application where you can create multiple choice questions and learn new knowledge from quizzes

multiple-choice-application

multivariate-statistical-analysis

This course includes multivariate location and scatter, principal component analysis (PCA), robustness and robust PCA, bivariate correspondence analysis, multiple correspondence analysis (MCA), canonical correlation analysis, discriminant analysis, statistical depth functions, classification and clustering. Software R is used in the exercises

mysql-for-data-analytics

network-security

numerical-analysis

numerical-methods-in-engineering

This course covers the theory behind classical numerical methods (for example: Newton-Raphson, Runge-Kutta and LU factorisation), and uses Matlab as a tool to solve, analyse and visualise computational problems and data. There is correction and improvement of existing code, while the validity and accuracy of numerical predictions are reflected

object-oriented-programming-with-cpp

This courses focus on the basic concepts of C++. Object oriented programming and generic programming in C++. C++ standard library. Tools for robust programming.

odin-project

operating-system

papers-for-molecular-design-using-dl

List of molecular design using Generative AI and Deep Learning

platform-security

prediction-and-time-series-analysis

principles-of-algorithmic-techniques

production-systems-modelling

This course covers queuing networks, optimization, regression analysis, and neural networks. The application of the methods to production systems planning and control: Hierarchical production planning, cost functions, Little's law, scheduling, lot sizes and set-ups, capacity planning, aggregate planning, facility location

programming-i