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Machine Learning
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The full NewUU syllabus — every topic, its major concepts and the lessons that cover them.
18
topics
151
lessons
84
formulas
~21 h
of study
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Foundations
1
Introduction to Machine Learning
Artificial Intelligence vs. Machine Learning vs. Deep Learning; motivation for ML; applications; learning from data; datasets, features, labels, parameters, hyperparameters; training, validation, and test sets; supervised, unsupervised, and reinforcement learning; classification vs. regression; parametric vs. nonparametric learning.
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2
Mathematical Foundations
Scalars, vectors, matrices, tensors; vector operations; inner products; matrix multiplication; norms and distances; linear transformations; probability; random variables; expectation; variance; covariance; conditional probability; Bayes' theorem
0/13
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3
Learning & Generalization
Dataset; Training vs. test error; generalization; overfitting; underfitting; model complexity; bias–variance trade-off; regularization; Ridge regression; Lasso regression; feature selection
0/11
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4
Learning as an Optimization Problem
Hypothesis/model class; parameters; loss functions; objective functions; empirical risk; expected risk; empirical risk minimization; gradient; partial derivatives; gradient descent; stochastic gradient descent; mini-batch optimization; learning rate; convergence.
0/12
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5
Similarity / Divergence Measures
F-divergences: KL, JS, Pearson χ², Neyman χ², Total Variation (TV), Hellinger divergences. Integral Probability Metrics (IPMs): Wasserstein distance, Maximum Mean Discrepancy (MMD), Bounded-Lipschitz (Fortet–Mourier) distance, Cramér distance, Kolmogorov distance.
0/13
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Supervised learning
6
Linear Regression
Linear models, least squares, closed-form solution, gradient descent, prediction error, Univariate vs. multivariate regression, Overfitting, underfitting, and regularization (Ridge/Lasso intro). Evaluation metrics: RMSE, MAE, R², F1 score
0/9
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7
Classification
Logistic regression, decision boundaries, probability-based classification, binary and multiclass classification
0/6
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8
Model Evaluation
Accuracy, precision, recall, F1-score, ROC/AUC, confusion matrices, cross-validation
0/6
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9
Overfitting and Regularization
Bias–variance trade-off, underfitting, overfitting, L1/L2 regularization, model complexity
0/6
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10
Decision Trees & Ensemble Learning
Decision trees, entropy, information gain, random forests, bagging, boosting
0/6
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11
Support Vector Machines
Maximum-margin classification, support vectors, kernels, nonlinear decision boundaries
0/5
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12
Nearest-Neighbor & Probabilistic Methods
k-NN, distance measures, Naïve Bayes, probabilistic classification
0/4
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Unsupervised learning
13
Unsupervised Learning
Clustering, k-means, hierarchical clustering, density-based clustering
0/4
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14
Dimensionality Reduction
High-dimensional data; curse of dimensionality; feature selection vs. feature extraction; Principal Component Analysis (PCA); covariance matrix; principal directions; variance maximization; reconstruction perspective; SVD; explained variance; visualization
0/10
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Neural & generative models
15
Introductory Neural Networks
Artificial neurons; perceptron; linear separability; multilayer perceptrons; hidden layers; activation functions; sigmoid; tanh; ReLU; forward propagation; loss functions; backpropagation; computational graphs; gradient-based learning
0/14
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16
Advanced Topics
Generative Models, Generative Adversarial Networks (GANs), Boltzmann Machine, Restricted Boltzmann Machine
0/8
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17
Distributed ML
Distributed Learning, Federated Learning, Distributed Generative Adversarial Networks (DGANs)
0/7
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Projects
18
Machine-Learning Applications & Projects
End-to-end ML pipelines, model comparison, interpretation, project presentations
0/4
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