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Mastering Machine Learning Theory and Practice
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Getting started
Introduction to the Course
Introduction
The Basic Idea of Machine Learning
History of Machine Learning
Mathematical Formulation of the Basic Learning Problem
Nonlinear Regression in High-Dimensions
Recent Advances
No Free Lunch, but Worth the Bite
Quiz
Scientific Programming with Python
Programming Environment
Basic Language Elements
Code Efficiency and Vectorization
Data Handling
Image Processing and Convolutional Filters
Quiz
Machine Learning with Sklearn
Overview
Classification Using Multiple Machine Learning Models
Performance Measures and Evaluations
Data Handling
Dimensionality Reduction, Feature Selection, and t-SNE
Decision Trees and Random Forests
Linear Support Vector Machines (SVMs)
Nonlinear Support Vector Machines (SVMs)
Validation of Support Vector Machines (SVMs)
Challenge: Which Classifier Works Well?
Solution: Which Classifier Works Well?
Quiz
Neural Networks and Keras
Neurons and the Threshold Perceptron
Multilayer Perceptron (MLP) and Keras
Applying MLP to the MNIST dataset
Representational Learning
Introduction to Convolutional Neural Networks (CNNs)
Applications of Convolutional Neural Networks (CNNs)
Object Detection
Challenges in Machine Learning
Challenge: Choose the Best Model
Solution: Choose the Best Model
Quiz
Regression and Optimization
Linear Regression and Gradient Descent
Error Surface and Challenges for Gradient Descent
Advanced Gradient Optimization (Learning)
Regularization: Ridge Regression and LASSO
Nonlinear Regression
Applying Gradient Descent to Neural Network Models
Stochastic Gradient Descent (SGD)
Artificial Neural Networks
Implementing a Multilayer Perceptron for the XOR Problem
Automatic Differentiation
Quiz
Basic Probability Theory
Random Numbers and Their Probability (Density) Function
Moments: Mean and Variance
Examples of Probability (Density) Functions
Cumulative Probability Function and the Central Limit Theorem
Density Functions of Multiple Random Variables
How to Combine Prior Knowledge With New Evidence
Markov Chain Monte Carlo
Quiz
Probabilistic Regression and Bayes Nets
Probabilistic Models
Learning in Probabilistic Models: Maximum Likelihood Estimate
Probabilistic Classification
Maximum A Posteriori (MAP) and Regularization with Priors
Bayes' Nets: Multivariate Causal Modeling
Probabilistic and Stochastic Neural Networks
Quiz
Generative Models
Modeling Classes
Supervised Generative Models
Naive Bayes
Self-Supervised Generative Models
Generative Neural Networks
Challenge: Sentiment Analysis Using Naive Bayes
Solution: Sentiment Analysis Using Naive Bayes
Challenge: K-Means Clustering
Solution: K-Means Clustering
Quiz
Cyclic Models and Recurrent Neural Networks
Overview
Sequence Processing
Basic Sequence Processing with MLP and RNN
Gated RNN and NLP
Other Gated Architectures and Attention
Markov Random Field Model and the Hopfield Model
The Boltzmann Machine
The Restricted Boltzmann Machine
Quiz
Reinforcement Learning
Formalization of the T-Maze Problem
Representation of the T-Maze roblem
Model-Based Reinforcement Learning
Model-Free Reinforcement Learning
Deep Reinforcement Learning
Actors and Actor-Critics
Reinforcement Learning in the Brain
Quiz
Artificial intelligence, the Brain, and Our Society
Overview
Different Levels of Modeling and the Brain
Machine Learning and Artificial Intelligence
The Impact of Machine Learning Technology on Society
Conclusion
Wrap Up
Copyright Information
Challenge: Choose the Best Model
Let's find out which model is the best.
We'll cover the following
Problem description
MNIST dataset
Architecture of the model
Challenge
Difference between the models
Results
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