Course: 42 Minutes

• Course Overview
• Prevent Neural Networks from Overfitting
• Multi-Label Learning Algorithms
• Deep Residual Learning for Image Recognition
• Transferable Features in Deep Neural Networks
• Large-Scale Video Classification
• Common Objects in Context
• Generative Adversarial Nets
• Scalable Nearest Neighbor Algorithms
• Face Alignment with Ensemble of Regression Trees
• Learning Deep Features for Scene Recognition
• Extreme Learning Machine (ELM)
• Exercise: Recognize Research Topics in ML and DL

Bayesian Methods for Machine

Bayesian Methods: Bayesian Concepts & Core Components

Course: 1 Hour, 1 Minute

• Course Overview
• Bayesian Probability and Statistical Inference
• Bayes' Theorem in Machine Learning
• Frequentist and Subjective Probability
• Probability Distribution
• Ingredients of Bayesian Statistics
• Bayesian Methods
• Bayesian Concepts in ML Modeling
• Prior Knowledge Distribution
• Bayesian Analysis Approach
• Exercise: Bayesian Statistics and Analysis

Bayesian Methods: Implementing Bayesian Model and Computation with PyMC

Course: 48 Minutes

• Course Overview
• Bayesian Learning
• Bayesian Model Types
• Probabilistic Programming
• Modeling with PyMC
• Bayesian Data Analysis Process
• Bayesian Data Analysis with PyMC
• Bayesian Computation Methods
• Markov Chain Simulation
• Implementing Markov Chain Simulation
• Finding Posterior Modes
• Exercise: Bayesian Modeling with PyMC

Bayesian Methods: Advanced Bayesian Computation Model

Course: 52 Minutes

• Course Overview
• Bayesian Model and Linear Regression
• Hierarchical Linear Model
• Probability Model
• Building Probability Models
• Non-Linear Model
• Gaussian Process
• Mixture Model
• Dirichlet Process Model
• Bayesian Modeling with PyMC
• Exercise: Implement Bayesian models