Lectures related to Knowledge Tracing

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Designing Learning Experiences: CS411 Class Project

Introduces the CS411 Class Project, where students design and test a lesson using learning theories.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Digital Education Masterclass: Training for Experts

Series provides training for experts in digital education, covering LMS, flipped classrooms, content integration, and online course launch.

Bayes' Theorem Overview

Introduces FROG, a tool for educators to enhance classroom interactivity.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.