Capstone Projects in Machine Learning

Capstone Projects in Machine Learning are of wide range but scholars may not be peculiar in each department is advisable to get professional solutions. Here we have shared some of our latest ML ideas and topics. Machine learning methods are widely classified into few kinds depending on their use case and the learning behavior. Below we list out categorized set of various machine learning techniques:

Supervised Learning Techniques: We describe that these are the trained techniques using labeled data, where the input and output are offered. Here, the technique learns to connect the input to the output.

  • Logistic Regression
  • Support Vector Machines (SVM)
  • Random Forest
  • Decision Trees
  • Artificial Neural Networks
  • Recurrent Neural Networks (RNN)
  • Gradient Boosting Machines (GBM)
  • Extreme Gradient Boosting (XGBoost)
  • AdaBoost
  • CatBoost
  • LightGBM
  • Naive Bayes
  • Ridge Regression
  • Elastic Net
  • Lasso Regression
  • Convolutional Neural Networks (CNN)
  • Linear Regression
  • K-Nearest Neighbors (KNN)
  • Perceptron
  • Linear Discriminant Analysis (LDA)
  • Quadratic Discriminant Analysis (QDA)
  • Long Short-Term Memory Networks (LSTM)

Capstone Topics in Machine Learning

Unsupervised Learning Methods: When dealing with unlabeled training data, we make use of unsupervised learning methods. They have the ability to detect patterns and designs inside the data.

  • Hierarchical Clustering
  • K-Means Clustering
  • Gaussian Mixture Models
  • Singular Value Decomposition (SVD)
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Principal Component Analysis (PCA)
  • Autoencoders
  • Apriori algorithm for Association Rule Learning
  • Latent Dirichlet Allocation (LDA)
  • Independent Component Analysis (ICA)
  • Self-Organizing Maps (SOM)

Semi-Supervised Learning Techniques: While the training process carries out with a huge amount of unlabeled data and a limited amount of labeled data, our project utilizes Semi-supervised learning techniques.

  • Label Spreading
  • Label Propagation
  • Semi-supervised Support Vector Machines (S3VM)

Reinforcement Learning Methodologies: We state that the reinforcement learning methodologies learn by communicating with platforms through the use of actions and viewing the outcomes through rewards or fines.

  • Q-Learning
  • Deep Q Network (DQN)
  • Actor-Critic Methods
  • Temporal Difference (TD) Learning
  • Proximal Policy Optimization (PPO)
  • Policy Gradient Methods
  • Trust Region Policy Optimization (TRPO)
  • SARSA (State-Action-Reward-State-Action)
  • Monte Carlo Tree Search (MCTS)

Ensemble Techniques: To enhance the accuracy and efficiency of our framework, the ensemble techniques integrate the forecasting of various machine learning methods.

  • Boosting
  • Voting Classifier
  • Bagging (Bootstrap Aggregating)
  • Stacking

We conclude that every method has its ability and is appropriate to specific types of issues and data modalities. Commonly, the trails, domain skills, chosen data, task types and mistakes help us to select proper technique.

Capstone Thesis Ideas In Machine Learning

Latest Machine Learning Capstone Thesis

Some of the Latest Machine Learning Capstone Thesis are shared by our experts you can get any field of capstone thesis customized to your needs. Best explanations will be given using innovative methods along with paper writing solutions.

  1. Concept of Intelligent Detection of DDoS Attacks in SDN Networks Using Machine Learning
  2. A Hybrid Classification Based on Machine Learning Classifiers to Predict Smart Indonesia Program
  3. A Machine Learning Framework for Data Filtering: A Case Study on Chandrayaan-1 SIR-2 Data
  4. Application of Data Mining Technology in Intelligent System of Machine Learning
  5. Multi-perspective Machine Learning (MPML) — A Machine Learning Model for Multi-faceted Learning Problems
  6. A Hypercuboid-Based Machine Learning Algorithm for Malware Classification
  7. Software Defect Prediction based on Machine Learning and Deep Learning
  8. Investigations on IoT Security System using Machine Learning Algorithm
  9. Emotion assessment using Machine Learning and low-cost wearable devices
  10. Towards Practical Privacy-Preserving Collaborative Machine Learning at a Scale
  11. A comparative study on recognizing human activities by applying diverse Machine Learning approaches
  12. Real-Time On-Chip Machine-Learning-Based Wearable Behind-The-Ear Electroencephalogram Device for Emotion Recognition
  13. Comparison of Machine Learning Algorithms for Raw Handwritten Digits Recognition
  14. Bitcoin Price Prediction Using Machine Learning and Deep Learning Algorithms
  15. Development of a Machine Learning Model For Big Data Analytics
  16. Data-Importance Aware Radio Resource Allocation: Wireless Communication Helps Machine Learning
  17. A Review on Machine Learning Approaches for Predicting the Effect of Device Parameters on Performance of Nanoscale MOSFETs
  18. Machine Learning based Authentication of loT Devices in Traffic Prediction for ITS
  19. Introducing Machine Learning in a Sophomore Signals and Systems Course
  20. Learnable Audio Encryption for Untrusted Outsourcing Machine Learning Services