Data Classification: Mixture Models

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Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Dimensionality Reduction: PCA & Autoencoders

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Machine Learning Programming: Introduction

Introduces the Machine Learning Programming course, covering MATLAB programming prerequisites and basics in Machine Learning, along with grading scheme and course materials.

Modeling Neurobiological Signals: Spikes & Firing Rate

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Statistical Interpretation of Artificial Neural Networks

Delves into the statistical interpretation of artificial neural networks, exploring the likelihood of data and maximizing model accuracy.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.