IF3270 Pembelajaran Mesin

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Overview

• Intro

  • Foundational Definitions and Scope of Machine Learning
  • Machine Learning Paradigms - Supervised Unsupervised and Reinforcement Learning
  • Supervised Learning Techniques - Regression and Classification
  • Neural Network Architectures for Specialized Tasks
  • Machine Learning System Design Workflow

• Ensemble Methods

  • • Foundations of Ensemble Learning - Concepts, Terminology, and the Wisdom of Crowds
  • • Bagging and Random Forests - Bootstrap Sampling, Model Diversity, and Majority Voting
  • • Heterogeneous Parallel Ensembles, Weighting Schemes, and Stacking Meta‑Learning
  • • Adaptive Boosting (AdaBoost) - Sequential Weak Learner Fusion via Instance Weighting
  • • Gradient Boosting and XGBoost - Residual‑Based Additive Modeling and Gradient‑Descent Optimization

• Perceptron

  • Biological Inspiration and Formal Definition of the Perceptron
  • Batch Gradient Descent and the Perceptron Learning (Delta) Rule
  • Stochastic and Mini‑Batch Gradient Descent for Perceptron Training
  • Logical Function Representation with Perceptrons (m‑of‑n, AND, OR, XOR)

• Feed-Forward Neural Network

  • Fundamentals, Motivation, and Application Domains of Artificial Neural Networks
  • Feed‑Forward Neural Network Architecture and Multi‑Layer Perceptron Design
  • Forward Propagation Mechanics in Feed‑Forward Neural Networks
  • Backpropagation Learning Algorithm and Gradient Computation for MLPs
  • Practical Implementation of Feed‑Forward Neural Networks with Keras and Parameter Accounting

• Convolutional Neural Network

  • Motivation and Fundamental Concepts of CNNs
  • Local Connectivity and Parameter Sharing Mechanisms
  • Convolutional Layer Operations and Hyperparameters
  • CNN Architectural Design Patterns
  • Backpropagation in Convolutional Networks

• Recurrent Neural Network (RNN Pt. 1)

  • RNN Fundamentals and Sequential Data Motivation
  • Forward Propagation Mechanics in RNNs
  • RNN Architectural Variations and Implementation

• Long Short-Term Memory (RNN Pt. 2)

  • Vanishing Gradient Problem in RNNs
  • LSTM Architecture Components and Gates
  • Forward Propagation in LSTM Networks
  • Backpropagation Through Time BPTT in RNNs
  • Encoder-Decoder Models in Sequence Learning

• Attention and Transformers

  • Traditional Encoder-Decoder Architecture in Sequence Models
  • Attention Mechanisms for Contextual Processing
  • Transformer Architecture and Self-Attention Mechanisms

• Reinforcement Learning

  • Reinforcement Learning Fundamentals and Motivation
  • Core Components of RL Agents
  • Categorization of RL Agent Architectures
  • Optimal Policy Formulation
  • Temporal Difference Learning Methods
  • Deep Q-Networks and Advanced Techniques