Courses

Machine learning lecture series by Sebastian Raschka covering machine learning fundamentals, nearest neighbors, Python, NumPy, scikit-learn, decision trees, ensemble methods, model evaluation, confidence intervals, cross-validation, statistical tests, and performance metrics.

Full lecture series from MIT OpenCourseWare by Gilbert Strang covering matrix methods for data analysis, signal processing, optimization, singular value decomposition, least squares, gradient descent, neural networks, convolutional networks, clustering, and the Julia language.

Deep learning lecture series by Alfredo Canziani covering neural networks, backpropagation, PyTorch, convolutional networks, recurrent networks, energy-based models, autoencoders, variational inference, attention, transformers, graph neural networks, planning, control, and self-supervised learning.

Full lecture series from Stanford Online by Andrew Ng covering supervised learning, linear regression, logistic regression, generalized linear models, naive Bayes, support vector machines, kernels, model selection, neural networks, expectation-maximization, PCA, ICA, reinforcement learning, Markov decision processes, and diagnostics.

Full lecture collection from Stanford covering image classification, neural networks, convolutional architectures, training techniques, detection, segmentation, generative models, and reinforcement learning. Spring 2017.

Big Data for Engineers lecture series by Ghislain Fourny from ETH Zurich Spring 2026, covering big data systems, cloud storage, distributed file systems, syntax, wide column stores, data models, MapReduce, Spark, large-scale performance, document stores, and querying trees.

Full lecture series from UC Berkeley CS 162 covering operating systems, systems programming, threads, processes, synchronization, scheduling, virtual memory, storage, filesystems, networking, distributed systems, and key-value storage.

Advanced algorithms lecture series from Harvard covering graduate-level algorithm design and analysis, randomized algorithms, approximation, graph algorithms, data structures, optimization, and computational complexity topics.

Information Retrieval lecture series by Ghislain Fourny from ETH Zurich Spring 2026, covering introduction to information retrieval, Boolean retrieval, term vocabulary, tolerant retrieval, index construction, index compression, vector space models, evaluation, probabilistic information retrieval, and language models.

Information Systems for Engineers lecture series by Ghislain Fourny from ETH Zurich Fall 2025, covering relational models, SQL data definition, relational algebra, SQL queries, database design theory, transactions, views, indices, data cubes, database architecture, and outlook topics.

UC Berkeley CS186 Introduction to Database Systems lecture playlist covering database system concepts, SQL, storage architecture, files, records, indexing, B+ trees, buffer management, relational operators, external sorting and hashing, query execution, and joins.

Mathematical modelling lecture series by Jason Bramburger covering modelling workflows, nondimensionalization, optimization, sensitivity analysis, Lagrange multipliers, linear programming, dynamical systems, numerical methods, regression, probability models, statistics, diffusion, and Markov chains.

Full lecture series from MIT OpenCourseWare by Gilbert Strang covering linear equations, matrices, vector spaces, orthogonality, determinants, eigenvalues, diagonalization, Fourier series, positive definiteness, singular value decomposition, and linear transformations.

Full lecture series from MIT OpenCourseWare covering sample spaces, random variables, distributions, limit theorems, and Bayesian inference. Spring 2018.

Full lecture series from Stanford Online covering counting, combinatorics, probability, conditional probability, random variables, expectation, variance, distributions, inference, modelling, maximum likelihood, Bayesian methods, machine learning applications, fairness, and advanced probability.

Full lecture series from Stanford Online by Stephen Boyd covering convex optimization, convex sets, convex functions, standard problem forms, duality, optimality conditions, numerical methods, and applications.

MIT OpenCourseWare lecture series covering financial markets, bond mathematics, linear algebra, probability theory, stochastic processes, regression analysis, rates products, principal component analysis, counterparty risk, portfolio management, volatility modeling, Black-Scholes, machine learning, stochastic calculus, and stochastic differential equations.