Week-long in-person teaching in summer 2023
Learn neurosience data analysis in Bucharest.
You can learn a lot of math with a bit of coding.
Mathematical programming gives you tools for learning, exploring, and discovering applied mathematics in areas including statistics, data science, and signal processing. This is why I use a combination of traditional instruction (lecture slides, words, diagrams) and hands-on programming in MATLAB and Python when teaching.
Visit this site for more information about the courses and application.
Deep learning
57+ hours of instruction on modern deep learning (using PyTorch), with LOTS of exercises and code-challenges to help you hone your DL skills. Includes a Python tutorial.
Stats and machine learning
35 hours of instruction (>50 hours in total, including exercises) on a rigorous deep-dive into statistics and machine learning. All concepts are implemented in Python and MATLAB.
Improving data quality in data analytics
Learn why, when, and how to maximize the quality of your data to optimize data-based decisions.
Linear algebra: theory and code
24+ hours of clear explanations of concepts in linear algebra, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular value decomposition.
Master calculus 1 using Python
Over 41 hours of instruction (including a Python intro tutorial). Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.
Master math with Python
31+ hours of instruction (>50 hours in total, including exercises) on how to use Python as a tool for learning concepts and visualizations in mathematics.
Code files for the linear algebra and calculus courses are also available on my github.
Linear Algebra: Theory, Intuition, Code
Strong focus on proofs, with coding and applications.
Linear Algebra for Data Science
Strong focus on coding and applications, with proofs.
Code files for the theory/intuition and data science books are available on my github.
Solved signal processing problems
11+ hours of instruction about applied signal-processing methods, including filtering, denoising, convolution, resampling, interpolation, and outlier detection.
Understand the Fourier transform
Theoretical and computational bases of the Fourier transform, and its implementation in modern applications in digital signal processing, data analysis, and image filtering. MATLAB and Python codes are included.
Analyzing neural time series data
40+ hours of YouTube video lectures that supplement my book "Analyzing Neural Time Series Analysis." Look for NEW ANTS playlists.
Solved challenges in neural time series analysis
Follow-up to the previous course with a lot more exercises and fully explained solutions.
PCA & multivariate signal processing
17 hours of instruction on theory, practice, and implementation of dimension-reduction and source-separation methods for multivariate data. MATLAB codes and sample datasets are included. Includes a 3-hour linear-algebra crash-course.
Essentials of Neuroscience with MATLAB
Free 10-hour course on using MATLAB for neuroscience data analysis, featuring various topics including computational modeling, fMRI, EEG, and spiking.
Analyzing Neural Time Series Data
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings.
Fundamentals of time-frequency analysis
This low-cost book focuses on practical implementations of time-frequency analyses in Matlab/Octave. The book explains time-frequency analyses through written explanations and many figures, rather than through opaque mathematical equations.
Code files for the neural time series book are available on my github.
Code files for the Fundamentals book are available from this zip file.
Master Python for scientific coding
32+ hours of instruction (>80 hours in total, including exercises) on using Python for scientific computing. No previous background in Python necessary!
Master math with Python
31+ hours of instruction (>50 hours in total, including exercises) on how to use Python as a tool for learning concepts and visualizations in mathematics.
MATLAB onramp
6+ hours of video instruction on getting started with MATLAB, debugging code, and developing a programming style. Ideal for a beginner who needs to get up-to-speed with MATLAB.
Master MATLAB through guided problem-solving
40 hours of video instruction on MATLAB programming instruction (longer if you do all the exercises!). Lessons revolve around specific applications in graphics, data analysis, image processing, import/export, and so on.
Learn image processing and GUIs while having fun in MATLAB
Learn key image processing techniques including spatial filtering, segmentation, transparency, and histogram equalization. You will also learn to create and program in graphical user interfaces (GUIs).
Generate and visualize data in Python and MATLAB
In this course you will learn how to simulate different kinds of data, including time series and images. Generated data can be used to validate data analysis methods.
Code files for the Onramp course are available on my github. Code files for other courses listed above are inside the course pages.
MATLAB for brain and cognitive scientists
This book is designed to bring scientists in psychology, neuroscience, and related fields to intermediate and advanced levels of programming proficiency.
Code files for the book are here, and solutions to all coding exercises are in this zip.