UUY-DTS 3132 units300 LevelFirst Semester

Machine Learning and Applications

B.Sc. Data Science, University of Uyo

UUY-DTS 313 (Machine Learning and Applications) is a 2-unit, 300 Level course taught in the First Semester at the University of Uyo, Akwa Ibom State, Nigeria.

The core machine learning course: supervised and unsupervised algorithms, how they are trained, how they are evaluated, and how they fail. The technical centrepiece of the degree.

On Areté, UUY-DTS 313 comes with

  • 3 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01The learning problem: supervised, unsupervised, semi-supervised and reinforcement learning
  2. 02Linear and logistic regression; loss functions and gradient descent
  3. 03Regularisation: ridge, lasso, and the bias–variance trade-off revisited
  4. 04k-nearest neighbours, Naive Bayes and support vector machines
  5. 05Decision trees, and ensembles: bagging, random forests and gradient boosting
  6. 06Unsupervised learning: k-means, hierarchical clustering, DBSCAN
  7. 07Dimensionality reduction: principal component analysis and t-SNE
  8. 08Model evaluation: confusion matrix, precision, recall, F1, ROC/AUC, cross-validation, hyperparameter tuning
  9. 09Introduction to neural networks: perceptrons, backpropagation, and where deep learning applies

Recommended textbooks

  • An Introduction to Statistical Learning — James, Witten, Hastie & Tibshirani

    2nd ed. — free at statlearning.com; the ideal level for this course

  • Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow — Aurélien Géron

    3rd ed. — the best practical companion

  • The Elements of Statistical Learning — Hastie, Tibshirani & Friedman

    2nd ed. — free; the rigorous reference for when you want the mathematics

How to pass UUY-DTS 313

  • Accuracy is a misleading metric on imbalanced data — learn precision, recall and the ROC curve properly, and be able to argue which matters for a given problem
  • Implement linear regression and k-means from scratch in NumPy once. After that, using scikit-learn is informed rather than magical
  • For every algorithm, learn its inductive bias — what it assumes about the data. That is what tells you when to reach for it
  • Compete in one beginner Kaggle competition; the feedback loop teaches evaluation discipline faster than any lecture

UUY-DTS 313 — questions and answers

What is UUY-DTS 313 at the University of Uyo?
UUY-DTS 313 (Machine Learning and Applications) is a 2-unit, 300 Level course taught in the First Semester at the University of Uyo, Akwa Ibom State, Nigeria. The core machine learning course: supervised and unsupervised algorithms, how they are trained, how they are evaluated, and how they fail. The technical centrepiece of the degree.
What topics are covered in UUY-DTS 313?
The UUY-DTS 313 outline covers 9 topics: The learning problem: supervised, unsupervised, semi-supervised and reinforcement learning; Linear and logistic regression; loss functions and gradient descent; Regularisation: ridge, lasso, and the bias–variance trade-off revisited; k-nearest neighbours, Naive Bayes and support vector machines; Decision trees, and ensembles: bagging, random forests and gradient boosting; Unsupervised learning: k-means, hierarchical clustering, DBSCAN; Dimensionality reduction: principal component analysis and t-SNE; Model evaluation: confusion matrix, precision, recall, F1, ROC/AUC, cross-validation, hyperparameter tuning; Introduction to neural networks: perceptrons, backpropagation, and where deep learning applies.
What textbooks are recommended for UUY-DTS 313?
Recommended reading for UUY-DTS 313: An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani; Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurélien Géron; The Elements of Statistical Learning by Hastie, Tibshirani & Friedman.
How do I pass UUY-DTS 313?
Accuracy is a misleading metric on imbalanced data — learn precision, recall and the ROC curve properly, and be able to argue which matters for a given problem Implement linear regression and k-means from scratch in NumPy once. After that, using scikit-learn is informed rather than magical For every algorithm, learn its inductive bias — what it assumes about the data. That is what tells you when to reach for it Compete in one beginner Kaggle competition; the feedback loop teaches evaluation discipline faster than any lecture
Where can I find UUY-DTS 313 lecture notes and past questions?
Areté (https://www.aretecyb.tech/courses/uuy-dts-313) carries 3 recommended textbooks for UUY-DTS 313, plus an AI tutor that has read the outline. It is free for University of Uyo students — sign in with your email, no password.

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