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COMP5318 · S1 2026
Applied Machine Learning
Detailed study guides distilled from weekly labs (Jupyter), lecture PDFs, and tutorials, with quizzes, flashcards, progress tracking, and EN / 中文 support.
Coverage
12 structured weeks
Toolkit
Study guides, maths, quizzes
Format
Lecture + notebook + tutorial synthesis
Mission control
Turn your study hub into a mastery loop: keep momentum, see progress, and jump straight to the right chapter.
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No learning streak yet. Open a chapter and complete a check to start one.
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Clear a chapter by building strong quiz, quick-check, and tutorial coverage.
Start Week 1
Use the study guide, then jump into maths and quizzes once the foundations are set.
Launch mission →Weekly atlas
Choose a week, then move from core ideas to maths, quizzes, and revision tools.
Week 1
Python, NumPy & Pandas
Language refresher, arrays, and tabular data — foundation for scikit-learn labs.
Ready
EN / 中文
Week 2
k-Nearest Neighbours
Instance-based classification plus 1R / PRISM rule learners, train/test splits, and feature scaling.
Ready
EN / 中文
Week 3
Linear & Logistic Regression
Least squares, Ridge/Lasso, and logistic models for classification.
Ready
EN / 中文
Week 4
Naive Bayes & Evaluation
Generative models, precision/recall/F1, cross-validation, and grid search.
Ready
EN / 中文
Week 5
Decision Trees & Ensembles
Splits, pruning, bagging, random forests, and boosting.
Ready
EN / 中文
Week 6
SVMs & PCA
Maximum margin, kernels, and dimensionality reduction.
Ready
EN / 中文
Week 7
Feedforward Neural Networks
Perceptron rule, backpropagation, ReLU, dropout, softmax and cross-entropy.
Ready
EN / 中文
Week 8
Convolutional & Recurrent Networks
CNN filters, stride, padding, pooling; RNN unrolling, BPTT, and LSTM gates.
Ready
EN / 中文
Week 9
Transformer & Attention
Seq2seq, attention mechanism, self-attention QKV, multi-head, positional encoding.
Ready
EN / 中文
Week 10
Clustering
K-means, GMM/EM, hierarchical clustering, DBSCAN, and evaluation metrics.
Ready
EN / 中文
Week 11
Markov Models & HMM
Markov property, HMM (A, B, π); evaluation (forward), decoding (Viterbi), learning (Baum-Welch).
Ready
EN / 中文
Week 12
Reinforcement Learning
MDPs, value functions, Q-Learning, Deep Q-Networks, and applications.
Ready
EN / 中文