Machine Learning

Objectives: Machine Learning

Complete Machine Learning Subtopics — Master Checklist

Complete Machine Learning Subtopics — Master Checklist

A comprehensive, structured HTML list of machine learning subtopics for study, teaching, and curriculum design.

1. Foundations & Math

  • Probability & Statistics
    • Probability basics: events, conditional probability, Bayes' theorem
    • Distributions: Bernoulli, Binomial, Gaussian, Poisson, Exponential, Multinomial
    • Expectation, variance, covariance, correlation
    • Law of large numbers, Central Limit Theorem
  • Linear Algebra
    • Vectors, matrices, matrix multiplication
    • Eigenvalues, eigenvectors, SVD
    • Matrix factorization, rank, positive-definite matrices
  • Calculus & Optimization
    • Derivatives, gradients, Hessian, directional derivatives
    • Chain rule (backpropagation)
    • Convexity, convex optimization basics
  • Information Theory
    • Entropy, cross-entropy, KL divergence, mutual information
  • Statistics for ML
    • Hypothesis testing, confidence intervals
    • Bias–variance tradeoff, sampling, bootstrapping

2. Data: Preparation & Feature Engineering

  • Data collection & storage (databases, CSV, parquet, streaming)
  • Data cleaning
    • Missing values (imputation strategies)
    • Outlier detection and handling
    • Noise reduction
  • Exploratory Data Analysis (EDA)
    • Univariate/multivariate analysis, visualization
    • Correlation analysis, pairplots
  • Feature engineering & selection
    • Encoding categorical variables: one-hot, ordinal, target encoding
    • Scaling & normalization: StandardScaler, MinMaxScaler, RobustScaler
    • Feature construction: polynomial features, interaction terms
    • Dimensionality reduction: PCA, t-SNE, UMAP
    • Feature selection: filter, wrapper, embedded methods (e.g., L1, tree-based, recursive feature elimination)
  • Data augmentation (images, text, audio)
  • Imbalanced data strategies: resampling, SMOTE, class weights
  • Pipeline design & reproducibility (scikit-learn pipelines, data versioning)

3. Supervised Learning (Regression & Classification)

A. Linear & Generalized Linear Models
  • Simple & Multiple Linear Regression
  • Polynomial Regression
  • Regularization: Ridge (L2), Lasso (L1), Elastic Net
  • Generalized Linear Models (GLMs): Logistic regression, Poisson regression
  • Quantile regression
B. Support Vector Machines & Kernels
  • SVM for classification & SVR for regression
  • Kernel methods: linear, polynomial, RBF, custom kernels
  • Kernel ridge regression
C. Tree-based Models
  • Decision trees (CART, ID3, C4.5)
  • Pruning, splitting criteria (Gini, entropy, MSE)
  • Random Forests
  • Gradient Boosting: AdaBoost, Gradient Boosting Machines
  • Modern GBM implementations: XGBoost, LightGBM, CatBoost
  • Tree-based feature importance & interpretation
D. Instance-based & Probabilistic
  • k-Nearest Neighbors (kNN) — classification & regression
  • Naïve Bayes (Gaussian, Multinomial, Bernoulli)
  • Nearest centroid, kernel density estimation
E. Probabilistic Models
  • Gaussian Processes (GPR/GPC)
  • Bayesian linear / logistic regression
F. Neural Networks (classical)
  • Perceptron, Multilayer Perceptron (MLP)
  • Activation functions, loss functions
  • Regularization: dropout, weight decay, early stopping
G. Specialised / Other Supervised Methods
  • Ensemble strategies: bagging, boosting, stacking, voting
  • Online learning: SGDClassifier, Passive-Aggressive algorithms
  • Metric learning & nearest-neighbor embeddings
  • Multi-output / multi-task regression & classification
  • Multi-label classification strategies (binary relevance, classifier chains)

4. Unsupervised Learning

  • Clustering
    • K-means, K-medoids, Mini-batch K-means
    • Hierarchical clustering (agglomerative, divisive)
    • DBSCAN, OPTICS, HDBSCAN
    • Gaussian Mixture Models (GMM)
  • Dimensionality reduction & manifold learning
    • PCA, SVD
    • t-SNE, UMAP, Isomap, LLE
  • Anomaly / Outlier detection
    • One-Class SVM, Isolation Forest, Local Outlier Factor
  • Association rule learning (Apriori, FP-Growth)
  • Topic modeling (LDA — Latent Dirichlet Allocation, NMF)
  • Self-supervised representation learning (contrastive methods)

5. Semi-supervised & Weak Supervision

  • Label propagation, label spreading
  • Pseudo-labeling, self-training
  • Co-training & tri-training
  • Weak supervision frameworks (Snorkel-style)

6. Reinforcement Learning (RL)

  • Core concepts: agent, environment, reward, policy, value
  • Dynamic programming: policy evaluation, iteration
  • Model-free methods: Monte Carlo, Temporal Difference (SARSA, Q-learning)
  • Policy gradients: REINFORCE, Actor-Critic
  • Deep RL: DQN, DDPG, PPO, A3C/A2C, SAC
  • Exploration strategies: epsilon-greedy, UCB, intrinsic motivation
  • Multi-agent RL and MARL topics

7. Deep Learning

  • Fundamentals
    • Neurons, layers, activation functions (ReLU, sigmoid, tanh, GELU)
    • Losses: MSE, cross-entropy, hinge loss
    • Backpropagation & computational graphs
  • Architectures
    • Feedforward / MLP
    • Convolutional Neural Networks (CNNs) & variants (ResNet, EfficientNet)
    • Recurrent Neural Networks (RNNs), LSTM, GRU
    • Transformers & attention mechanisms
    • Graph Neural Networks (GNNs)
    • Autoencoders, VAEs, GANs
  • Regularization & generalization
    • Dropout, batch normalization, layer normalization
    • Data augmentation, label smoothing
  • Advanced techniques
    • Transfer learning & fine-tuning
    • Self-supervised learning (SimCLR, MoCo, BYOL)
    • Contrastive learning, metric learning
    • Neural architecture search (NAS)

8. Probabilistic & Bayesian Methods

  • Bayesian inference fundamentals
  • Bayesian networks & graphical models
  • Markov Chain Monte Carlo (MCMC): Metropolis-Hastings, Gibbs sampling
  • Variational inference
  • Gaussian processes (covered earlier but included here)

9. Time Series & Sequential Models

  • Time series basics: stationarity, seasonality, autocorrelation
  • Classical models: AR, MA, ARMA, ARIMA, SARIMA
  • State-space models, Kalman filters
  • Prophet (trend & seasonality modeling)
  • Sequence models: RNNs, LSTMs, Transformers for sequences (Temporal Fusion Transformer)
  • Forecasting evaluation: MAPE, MAE, RMSE, sMAPE

10. Natural Language Processing (NLP)

  • Text preprocessing: tokenization, stemming, lemmatization, stopwords
  • Feature representations: bag-of-words, TF-IDF, word embeddings (Word2Vec, GloVe), contextual embeddings (BERT, GPT)
  • Sequence models: RNNs, LSTM, attention-based models
  • Transformers, pretraining and fine-tuning (BERT, RoBERTa, GPT, T5)
  • Common tasks: classification, NER, POS tagging, QA, summarization, translation

11. Computer Vision (CV)

  • Image preprocessing & augmentation
  • CNN architectures, transfer learning in vision
  • Object detection: R-CNN family, YOLO, SSD
  • Segmentation: semantic (U-Net), instance (Mask R-CNN)
  • Image generation: GANs, diffusion models
  • Keypoint detection, pose estimation

12. Model Evaluation & Metrics

  • Regression metrics: MSE, RMSE, MAE, R², adjusted R²
  • Classification metrics: accuracy, precision, recall, F1, AUC-ROC, log loss
  • Confusion matrix analysis
  • Cross-validation strategies: k-fold, stratified, time-series CV
  • Statistical tests for model comparison (paired t-test, McNemar's test, bootstrap)

13. Optimization & Training Techniques

  • Gradient descent variants: batch, mini-batch, stochastic
  • Adaptive optimizers: AdaGrad, RMSprop, Adam, AdamW
  • Learning rate schedules & warm restarts
  • Gradient clipping, mixed precision training
  • Hyperparameter tuning: grid search, random search, Bayesian optimization (HyperOpt, Optuna), population-based training

14. Ensemble Methods & Meta-learning

  • Bagging & bootstrap aggregation
  • Boosting families: AdaBoost, gradient boosting, and modern GBMs
  • Stacking (stacked generalization)
  • Model selection ensembles, snapshot ensembles
  • Meta-learning: few-shot learning, learning-to-learn

15. Interpretability & Explainable AI (XAI)

  • Feature importance methods (permutation, tree-based importance)
  • Local explanations: LIME, SHAP
  • Global explanation techniques: surrogate models, partial dependence plots, ALE
  • Counterfactual explanations

16. Model Deployment, Production & MLOps

  • Model serialization (pickle, joblib, ONNX, SavedModel)
  • APIs & serving (Flask, FastAPI, TensorFlow Serving, TorchServe)
  • Containerization & orchestration (Docker, Kubernetes)
  • CI/CD for ML, reproducibility, experiment tracking (MLflow, Weights & Biases)
  • Monitoring, model drift detection, A/B testing
  • Data pipelines & ETL for ML (Airflow, Prefect, Dagster)

17. Ethics, Fairness & Privacy

  • Bias detection & mitigation strategies
  • Fairness definitions (demographic parity, equalized odds)
  • Privacy-preserving ML: federated learning, differential privacy, homomorphic encryption
  • Responsible AI guidelines & governance

18. Tools, Libraries & Ecosystem

  • Python ecosystem: NumPy, Pandas, SciPy, scikit-learn
  • Deep learning frameworks: TensorFlow, PyTorch, JAX
  • GBM libraries: XGBoost, LightGBM, CatBoost
  • Model serving & MLOps: MLflow, BentoML, Seldon, Kubeflow
  • Data tools: Dask, Apache Spark, DuckDB
  • Visualization: Matplotlib, Plotly, Seaborn, Altair

19. Research Topics & Advanced Areas

  • Generative models: diffusion models, advanced GAN variants
  • Causality & causal inference
  • Continual learning & lifelong learning
  • Meta-learning & few-shot learning
  • Quantum machine learning (emerging)
  • Robustness, adversarial examples and defenses

20. Suggested Learning Path & Projects

  1. Start with foundations: probability, linear algebra, calculus
  2. Implement basic algorithms from scratch: linear regression, logistic regression, kNN
  3. Move to scikit-learn workflows and pipelines
  4. Explore tree-based models and GBMs for tabular data
  5. Learn deep learning fundamentals and CNNs for vision
  6. Study transformers and modern NLP
  7. Practice MLOps: deploy a model behind an API and monitor it
  8. Build end-to-end projects: prediction app, image classifier, chatbot, recommender
  9. Read research papers and reproduce results

Use this checklist to design courses, lessons, or self-study plans. Each bulletpoint can be expanded into lessons, exercises, mathematical derivations, and coding labs.

Complete Machine Learning Notes – ML Algorithms Explained

Machine Learning (ML) Full Notes

Complete Guide in English & Swahili with Formulas, Diagrams, and Examples

1. Introduction / Utangulizi

Machine Learning (ML) is a branch of Artificial Intelligence (AI) where systems learn patterns from data and make decisions or predictions.
Swahili: Machine Learning ni tawi la Artificial Intelligence ambapo mifumo inajifunza mifumo kutoka kwenye data na kufanya maamuzi au utabiri.

2. Types of ML / Aina za ML

  • Supervised Learning / Kujifunza kwa Usimamizi: Model learns from labeled data (input-output pairs).
    Example: Predicting house prices based on area.
  • Unsupervised Learning / Kujifunza Bila Usimamizi: Model finds patterns in unlabeled data.
    Example: Customer segmentation in a store.
  • Reinforcement Learning / Kujifunza kwa Kuridhishwa: Model learns by trial and error to maximize rewards.
    Example: Self-driving car navigation.

3. Supervised Learning / Kujifunza kwa Usimamizi

Algorithms under supervised learning include:

  • Linear Regression / Mlinganyo wa Kimsingi: Predicts a continuous value using formula:
    • y = β0 + β1x + ε

    Symbols: β0=intercept, β1=slope, ε=error term

    Example: Predicting house price based on area.
  • Logistic Regression / Mlinganyo wa Kolojisti: Used for binary classification. Formula:
    • P(Y=1|X) = 1 / (1 + e- (β0 + β1x))

    Symbol meanings: P = probability, e = Euler's number, β = coefficients

    Example: Predict if a student passes/fails based on study hours.
  • Decision Tree / Mti wa Uamuzi: Splits data into branches based on conditions. Visualized as:
    • Root Leaf1 Leaf2
    Example: Predict if customer will buy a product based on age & income.
  • Random Forest / Msitu wa Nasibu: Ensemble of Decision Trees to improve accuracy.
  • Support Vector Machine (SVM) / Mashine ya Kutumia Msaada: Finds a hyperplane to separate classes. Formula for 2D hyperplane: w·x + b = 0

4. Unsupervised Learning / Kujifunza Bila Usimamizi

  • Clustering / Kundi: Group similar data points. Algorithm examples: K-Means, Hierarchical Clustering.
  • Dimensionality Reduction / Kupunguza Vipimo: Reduce features while preserving information. Examples: PCA, t-SNE.
    • Example: Customer clustering based on purchase behavior.

5. Neural Networks / Mitandao ya Neva

Neural networks are inspired by human brain neurons. Formula for a neuron:

y = f(Σ wixi + b)

Symbols: f = activation function, w = weight, x = input, b = bias

X1 X2 Neuron Output

6. ML Algorithm Comparison / Linganisha Algorithimu za ML

Algorithm Type / Aina Strength / Faida Weakness / Hasara Example / Mfano
Linear Regression Supervised Simple, interpretable Fails on non-linear data Predict house price
Decision Tree Supervised Interpretable, handles non-linear Prone to overfitting Customer purchase prediction
K-Means Unsupervised Easy to implement, scalable Needs number of clusters predefined Market segmentation
Neural Networks Supervised/Deep Learning Can approximate complex functions Needs large data, computationally expensive Image recognition

7. Conclusion / Hitimisho

Machine learning has diverse algorithms for different tasks. Choosing the right algorithm depends on the problem type, data size, and interpretability needs.
Swahili: Machine learning ina algorithimu tofauti kwa kazi tofauti. Kuchagua algorithm sahihi kunategemea aina ya tatizo, ukubwa wa data, na uelewa unaohitajika.

Reference Book: N/A

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