Merge pull request #482 from mbeps/updates

Added new university projects

app/about/page.tsx

@@ -11,17 +11,18 @@ import CertificateDatabaseKeys from "@/database/Certificates/CertificateDatabase
 import companyDatabaseMap from "@/database/Companies/CompanyDatabaseMap";
 import CourseDatabaseKeys from "@/database/Courses/CourseDatabaseKeys";
 import courseDatabaseMap from "@/database/Courses/CourseDatabaseMap";
-import CourseInterface from "@/database/Courses/CourseInterface";
+import type CourseInterface from "@/database/Courses/CourseInterface";
 import ProjectDatabaseKeys from "@/database/Projects/ProjectDatabaseKeys";
 import RoleDatabaseKeys from "@/database/Roles/RoleDatabaseKeys";
 import rolesDatabase from "@/database/Roles/RoleDatabaseMap";
-import RoleInterface from "@/database/Roles/RoleInterface";
+import type RoleInterface from "@/database/Roles/RoleInterface";
 import type { Metadata } from "next";
 import Image from "next/image";
 import { notFound } from "next/navigation";
 
-const aboutContent: string | undefined =
-  getMarkdownFromFileSystem(`public/about/long.md`)?.content;
+const aboutContent: string | undefined = getMarkdownFromFileSystem(
+  "public/about/long.md"
+)?.content;
 
 export const metadata: Metadata = {
   title: `${developerName} - About Me`,

database/Modules/ModuleDatabaseKeys.ts

@@ -32,14 +32,14 @@ enum ModuleDatabaseKeys {
   RHUL_SecurityManagement = "IY3501",
 
   // King's College London
-  KCL_ArtificialIntelligenceReasoningAndDecisionMaking = "6CCS3AIN",
+  KCL_PatternRecognitionNeuralNetworksDeepLearning = "7CCSMPNN",
   KCL_MachineLearning = "6CCS3ML1",
+  KCL_DataMining = "7CCSMDM1",
+  KCL_OptimizationMethods = "7CCSMOME",
+  KCL_ArtificialIntelligenceReasoningAndDecisionMaking = "6CCS3AIN",
   KCL_AgentsAndMultiAgentSystems = "7CCSMAMS",
   KCL_ComputerVision = "7CCSMCVI",
-  KCL_DataMining = "7CCSMDM1",
   KCL_PhilosophyAndEthicsOfArtificialIntelligence = "7CCSMEAI",
-  KCL_OptimizationMethods = "7CCSMOME",
-  KCL_PatternRecognitionNeuralNetworksDeepLearning = "7CCSMPNN",
   KCL_IndividualProject = "7CCSMPRJ",
 }
 

database/Modules/ModuleDatabaseMap.ts

@@ -1050,6 +1050,7 @@ const modulesMap: Database<ModuleInterface> = {
       "Implementing generative models like GANs to create synthetic data",
       "Applying transfer learning to leverage pretrained models for new tasks",
     ],
+    relatedMaterials: [ProjectDatabaseKeys.HandWrittenDigitClassifier],
   },
   [ModuleDatabaseKeys.KCL_MachineLearning]: {
     name: "Machine Learning",
@@ -1098,6 +1099,9 @@ const modulesMap: Database<ModuleInterface> = {
       "Implementing learning from demonstration techniques",
       "Addressing challenges in machine learning such as overfitting and bias",
     ],
+    relatedMaterials: [
+      ProjectDatabaseKeys.MachineLearningPacmanClassifierCoursework,
+    ],
   },
   [ModuleDatabaseKeys.KCL_IndividualProject]: {
     name: "Individual Project",

database/Projects/ProjectDatabaseKeys.ts

@@ -7,6 +7,7 @@
  * @see {@link projectMap} at `database/projects.ts`
  */
 enum ProjectDatabaseKeys {
+  //^ Full Stack Projects
   CircusDiscussions = "circus-discussions",
   RingmasterMessaging = "ringmaster-messaging",
   MagicianAI = "magician-ai",
@@ -15,6 +16,8 @@ enum ProjectDatabaseKeys {
   Quizmify = "quizmify",
   SideshowArticles = "sideshow-articles",
   Noodle = "noodle",
+
+  //^ Backend Projects
   SymphonyTranslateBot = "symphony-translate-bot",
   SymphonyWebhookBot = "symphony-webhook-bot",
   SymphonyCobaGPTBot = "symphony-coba-gpt-bot",
@@ -31,6 +34,10 @@ enum ProjectDatabaseKeys {
   DjangoAuthentication = "django-authentication",
   ClerkAuthentication = "clerk-authentication",
   Auth0Authentication = "auth0-authentication",
+
+  //^ Artificial Intelligence Projects
+  MachineLearningPacmanClassifierCoursework = "machine-learning-pacman-classifier-coursework",
+  HandWrittenDigitClassifier = "hand-written-digit-classifier",
   MachineLearningAlgorithms = "machine-learning-algorithms",
   ArtificialIntelligenceReinforcementLearning = "artificial-intelligence-reinforcement-learning",
   AdultIncomePrediction = "adult-income-prediction",
@@ -45,6 +52,7 @@ enum ProjectDatabaseKeys {
   ComputerVisionImageSegmentation = "computer-vision-image-segmentation-assignment",
   ComputerVisionQuizzes = "computer-vision-quizzes",
   OsmosGame = "osmos-game",
+
   SearchingAndSortingAlgorithms = "searching-and-sorting-algorithms",
   AutomatedSetup = "automated-setup",
   Leetcode = "leetcode",

database/Projects/ProjectDatabaseMap.ts

@@ -9,7 +9,6 @@ import skillDatabaseMap from "../Skills/SkillDatabaseMap";
 import SkillCategoriesEnum from "@/enums/Skill/SkillCategoriesEnum";
 import ModuleDatabaseKeys from "@/database/Modules/ModuleDatabaseKeys";
 import CertificateDatabaseKeys from "../Certificates/CertificateDatabaseKeys";
-import BlogDatabaseKeys from "../Blogs/BlogDatabaseKeys";
 import RoleDatabaseKeys from "../Roles/RoleDatabaseKeys";
 import ProjectTypeEnum from "@/enums/Project/ProjectTypeEnum";
 
@@ -372,7 +371,7 @@ const projectMap: Database<ProjectInterface> = {
     type: ProjectTypeEnum.Academic,
   },
 
-  //^ Symphony Bots
+  //^ Backend Web Development Projects
   [ProjectDatabaseKeys.SymphonyTranslateBot]: {
     name: `Symphony Translate Bot`,
     description: `
@@ -750,8 +749,6 @@ const projectMap: Database<ProjectInterface> = {
     archived: true,
     type: ProjectTypeEnum.Professional,
   },
-
-  //^ Backend Web Development Projects
   [ProjectDatabaseKeys.FlaskForumBackend]: {
     name: `Flask Forum Backend`,
     description: `
@@ -923,6 +920,42 @@ const projectMap: Database<ProjectInterface> = {
   },
 
   //^ Artificial Intelligence Projects
+  [ProjectDatabaseKeys.HandWrittenDigitClassifier]: {
+    name: "Handwritten Digit Classifier",
+    description: `
+      A handwritten digit classifier using built using a Convolutional Neural Network (CNN).
+      Used various techniques such as data augmentation, batch normalisation, and dropout to improve the model's performance.
+      `,
+    repositoryURL:
+      "https://github.com/mbeps/pattern-recognition-neural-network-coursework-1",
+    category: ProjectCategoriesEnum.ArtificialIntelligence,
+    type: ProjectTypeEnum.Academic,
+    relatedMaterials: [
+      ModuleDatabaseKeys.KCL_PatternRecognitionNeuralNetworksDeepLearning,
+    ],
+    skills: [
+      SkillDatabaseKeys.Python,
+      SkillDatabaseKeys.Keras,
+      SkillDatabaseKeys.TensorFlow,
+      SkillDatabaseKeys.Matplotlib,
+      SkillDatabaseKeys.Jupyter,
+      SkillDatabaseKeys.Git,
+      SkillDatabaseKeys.GitHub,
+      SkillDatabaseKeys.Poetry,
+      SkillDatabaseKeys.Black,
+
+      SkillDatabaseKeys.ProblemSolving,
+      SkillDatabaseKeys.ProjectManagement,
+      SkillDatabaseKeys.CriticalThinking,
+      SkillDatabaseKeys.Creativity,
+      SkillDatabaseKeys.Adaptability,
+      SkillDatabaseKeys.ObjectOrientedProgramming,
+      SkillDatabaseKeys.Algorithms,
+    ],
+    thumbnailImage: addProjectThumbnail(
+      ProjectDatabaseKeys.HandWrittenDigitClassifier
+    ),
+  },
   [ProjectDatabaseKeys.AdultIncomePrediction]: {
     name: "Adult Income Prediction",
     description: `
@@ -993,6 +1026,41 @@ const projectMap: Database<ProjectInterface> = {
       ProjectDatabaseKeys.HousePricePrediction
     ),
   },
+  [ProjectDatabaseKeys.MachineLearningPacmanClassifierCoursework]: {
+    name: "Pacman Neural Network Classifier",
+    description: `
+      Built a neural network from scratch to detect Pacman's direction in the game.
+      Used various techniques such as batch normalisation, dropout, momentum, learning rate decay and more to improve the model's performance.
+      `,
+    repositoryURL:
+      "https://github.com/mbeps/machine-learning-pacman-classifier-coursework",
+    category: ProjectCategoriesEnum.ArtificialIntelligence,
+    type: ProjectTypeEnum.Academic,
+    relatedMaterials: [ModuleDatabaseKeys.KCL_MachineLearning],
+    skills: [
+      SkillDatabaseKeys.Python,
+      SkillDatabaseKeys.Git,
+      SkillDatabaseKeys.GitHub,
+      SkillDatabaseKeys.Poetry,
+      SkillDatabaseKeys.Black,
+
+      SkillDatabaseKeys.LinearAlgebra,
+      SkillDatabaseKeys.Probability,
+      SkillDatabaseKeys.Statistics,
+      SkillDatabaseKeys.Calculus,
+
+      SkillDatabaseKeys.ProblemSolving,
+      SkillDatabaseKeys.ProjectManagement,
+      SkillDatabaseKeys.CriticalThinking,
+      SkillDatabaseKeys.Creativity,
+      SkillDatabaseKeys.Adaptability,
+      SkillDatabaseKeys.ObjectOrientedProgramming,
+      SkillDatabaseKeys.Algorithms,
+    ],
+    thumbnailImage: addProjectThumbnail(
+      ProjectDatabaseKeys.MachineLearningPacmanClassifierCoursework
+    ),
+  },
   [ProjectDatabaseKeys.MachineLearningAlgorithms]: {
     name: "Machine Learning Algorithms",
     description: `

public/projects/hand-written-digit-classifier/features.md

@@ -0,0 +1,66 @@
+## Network Architecture
+The network follows a progressive deepening structure, with each block increasing in complexity to learn more sophisticated features.
+
+### Block 1
+- Two Conv2D layers (32 filters, 3×3 kernel) - Captures basic edges and shapes
+- BatchNormalisation after each Conv2D - Stabilises training
+- MaxPooling (2×2) - Reduces spatial dimensions and computational load
+- SpatialDropout2D (20%) - Prevents feature map co-adaptation
+
+### Block 2
+- Two Conv2D layers (96 filters, 3×3 kernel) - Learns intermediate-level patterns
+- BatchNormalisation after each Conv2D - Maintains consistent feature scaling
+- MaxPooling (2×2) - Further dimension reduction
+- SpatialDropout2D (20%) - Continues regularisation
+
+### Block 3
+- Two Conv2D layers (128 filters, 3×3 kernel, 'same' padding) - Identifies complex digit features
+- BatchNormalisation after each Conv2D - Normalises deeper features
+- MaxPooling (2×2) - Final spatial reduction
+- SpatialDropout2D (20%) - Ensures robust feature learning
+
+### Dense Layers
+- Flatten layer - Converts 2D features to 1D
+- Dense layer (1050 units) with L2 regularisation - Rich feature combination
+- BatchNormalisation - Stabilises deep network training
+- Dropout (50%) - Prevents overfitting
+- Output layer (10 units, softmax) - Produces digit probabilities
+
+## Data Augmentation
+
+### Static Augmentation (Albumentations)
+- ElasticTransform - Simulates natural handwriting deformations
+- GaussNoise - Adds resilience to image noise
+- CoarseDropout - Improves robustness to missing parts
+- RandomBrightnessContrast - Handles varying image qualities
+- Image Inversion - Adapts to different digit colours
+
+### Real-time Augmentation (ImageDataGenerator)
+- Rotation - Handles tilted handwriting
+- Width/Height shifts - Accounts for different digit positions
+- Zoom range - Manages varying digit sizes
+
+## Training Strategy
+
+### Optimisation
+- Adam optimiser with gradient clipping - Prevents explosive gradients
+- Initial learning rate: 1e-3 - Balanced between speed and stability
+- Batch size: 384 - Provides stable gradient estimates
+
+### Training Callbacks
+- Early Stopping - Prevents overfitting by monitoring validation loss
+- Model Checkpoint - Preserves best model during training
+- ReduceLROnPlateau - Adapts learning rate when progress plateaus
+
+### Regularisation Techniques
+- BatchNormalisation - Stabilises training throughout the network
+- SpatialDropout2D - Specifically designed for convolutional features
+- Standard Dropout - Prevents dense layer overfitting
+- L2 regularisation - Controls weight growth
+
+## Dataset Management
+- Multiple dataset combination - Increases training diversity
+- Image standardisation (28×28) - Ensures consistent input size
+- Value normalisation [0,1] - Stabilises network training
+- 90-10 split - Provides sufficient validation data
+- Fixed random seed - Ensures reproducible results

public/projects/machine-learning-pacman-classifier-coursework/features.md

@@ -0,0 +1,35 @@
+## Core Classification Architecture
+- Feed-forward neural network with configurable hidden layers
+- ReLU activation functions for hidden layers
+- Softmax output layer for 4-class movement prediction
+- He initialisation for weights
+
+## Optimisation Techniques
+- Mini-batch gradient descent
+- Momentum-based updates with velocity tracking
+- Inverse scaling learning rate decay
+- L2 regularisation
+- Gradient clipping with norm thresholding
+
+## Regularisation Methods
+- Batch Normalisation
+  - Running mean/variance tracking
+  - Learnable scale (gamma) and shift (beta) parameters
+  - Training/inference mode handling
+- Dropout
+  - Inverted dropout scaling
+  - Configurable dropout rate
+
+## Training Management
+- Early stopping with validation monitoring
+- Automatic hyperparameter optimisation via grid search
+- Train/validation/test split (80/10/10)
+- Best model checkpointing
+
+## Implementation Features
+- Type hints throughout codebase
+- Numerical stability safeguards
+- Comprehensive error handling
+- Memory efficient operations
+- Legal move validation and filtering
+- Fallback strategies for edge cases