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Activation Functions (1)

AdaBoost (1)

Backpropagation (1)

Bias-Variance Tradeoff (1)

Boosting (1)

CNNs (1)

Classification (2)

Classification Metrics (1)

Clustering (1)

Collaborative Filtering (1)

Cross Validation (1)

Decision Trees (1)

Deep Learning (2)

Elastic Net (1)

Ensemble Methods (2)

Exploration-Exploitation (1)

Feature Engineering (1)

Feature Selection (1)

Gradient Descent (2)

Hinge Loss (1)

Image Processing (1)

K-means (1)

Kernels (1)

LSTM (1)

Lasso Regression (1)

Least Squares (1)

Linear Cassification (1)

Linear Regreesion (1)

Logistic Regression (1)

Loss Function (1)

Matrix Factorization (1)

Model Assessment (1)

Model Selection (1)

Multi-Armed Bandit (1)

Neural Networks (3)

Normal Equation (1)

Perceptron (1)

Random Forest (1)

Recommender Systems (1)

Recurrent Neural Networks (1)

Regression Metrics (1)

Regularization (1)

Reinforcement Learning (1)

Ridge Regression (1)

Sigmoid Function (1)

Squared Loss (1)

Training Error (1)

Unsupervised Learning (1)

CS 334 Machine Learning

1 Linear Classification

Linear Cassification

Loss Function

Training Error

Perceptron

This lecture discusses the linear classification, a fundamental concept in machine learning. It introduces loss functions, training error, and the perceptron algorithm.

2 Gradient Descent on Classification

Classification

Gradient Descent

Hinge Loss

This lecture discusses the gradient descent algorithm and its application to classification problem.It introduces the hinge loss function and the update rule for gradient descent.

3 Linear Regression

Linear Regreesion

Gradient Descent

Squared Loss

Least Squares

Normal Equation

This lecture discusses the linear regression, a fundamental concept in machine learning. It introduces the squared loss function and the update rule for gradient descent. It also derived the closed-form solution for linear regression using matrix notation.

4 Regularization

Regularization

Bias-Variance Tradeoff

Ridge Regression

Lasso Regression

Elastic Net

This lecture starts from the bias-variance tradeoff, and then introduces regularization as a way to control the tradeoff. We will discuss the \(L_2\) regularization (Ridge Regression), \(L_1\) regularization (Lasso Regression), and Elastic Net.

5 Logistic Regression

Logistic Regression

Classification

Sigmoid Function

This lecture introduces the logistic regression model, which is used for binary classification. We will discuss the sigmoid function, the likelihood function, and the cost function.

6 Model Selection and Model Assessment

Model Selection

Model Assessment

Classification Metrics

Regression Metrics

Cross Validation

This lecture discusses the model assessment and model selection process. We will cover classification performance metrics, regression metrics, model assessment process, and model selection.

7 Feature Selection and Kernels

Feature Engineering

Feature Selection

Kernels

This lecture discusses the feature selection and kernel methods. We will cover feature engineering and selection methods, kernel methods, and kernel tricks.

8 Decision Trees and Random Forest

Decision Trees

Ensemble Methods

Random Forest

This lecture discusses the basics od decision trees including how to build a decision tree. It also introduces ensemble methods and discusses the random forest algorithm as an example of ensemble methods.

9 Boosting

Boosting

AdaBoost

Ensemble Methods

This lecture discusses Boosting, a powerful ensemble learning technique that combines weak learners to create a strong learner.

10 Introduction to Neural Networks

Neural Networks

Backpropagation

Activation Functions

This lecture discusses the basics of neural networks, including their architecture, activation functions, and training process. It also covers the concept of backpropagation and its role in optimizing neural networks.

11 Convolutional Neural Networks

Neural Networks

CNNs

Image Processing

Deep Learning

This lecture discusses the architecture and functioning of Convolutional Neural Networks (CNNs), including their layers, operations, and applications in image processing and computer vision. It also covers the concept of pooling layers and their role in reducing dimensionality.

12 Recurrent Neural Networks

Neural Networks

Recurrent Neural Networks

LSTM

Deep Learning

This lecture discusses the basics of recurrent neural networks (RNNs), including their architecture, training process, and applications. It also covers the concept of long short-term memory (LSTM) networks and their role in handling sequential data.

13 Reinforcement Learning

Reinforcement Learning

Multi-Armed Bandit

Exploration-Exploitation

This lecture discusses the basics of reinforcement learning, including the concepts of agents, environments, rewards, and policies. It also covers the exploration-exploitation trade-off and multi-armed bandit problem.

14 Recommender Systems

Recommender Systems

Collaborative Filtering

Matrix Factorization

This lecture discusses the basics of recommender systems, focusing on collaborative filtering. It introduces the nearest neighbor algorithm and matrix factorization techniques, including low-rank approximation via alternative minimization algorithm.

15 Clustering

Clustering

Unsupervised Learning

K-means

This lecture discusses the basics of clustering, focusing on the k-means algorithm. It covers the algorithm’s initialization, convergence, and the concept of local minima.