# Planning and summaries

Please note that class planning is subject to adjustments.

## Files

Class notes are avaliable as a single file here, in pdf format. These notes do not substitute reading the recommended literature.

LectureNotes.pdf Last updated on 2019-10-14.

**Note:** these notes will be updated during the semester. It is best not to print them and to check this page regularly for updates.

Slides in html format and can be viewed directly from this page. Use the ← and → keys to change slides or Esc to select specific slides.

Lecture videos will be available after each lecture.Each lecture video is also linked from the corresponding summary.

Lecture videos for this year are availale in this YouTube playlist: Aprendizagem Automatica 2019/20

Lecture videos from 2018/19 are availale in this YouTube playlist:Aprendizagem Automática, 2018/19.

Lecture videos from 2017/18 are availale in this YouTube playlist: Aprendizagem Automática, 2017/18.

## Summaries

### Lectures 1 and 2, 2019-09-10.

- Summary
- Introduction.Supervised learning, minimization (least squares), polynomial regression
- Lecture 1 (HTML)Lecture 1 (PDF), Lecture 1 notes (PDF), Video
- Lecture 2 (HTML), Lecture 2 (PDF), Lecture 2 notes (PDF), Video
- Code for the polynomial regression example: L2.zip

### Lectures 3 and 4, 2019-09-17.

- Summary
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Lecture 3 (HTML),Lecture 3 (PDF), Lecture 3 notes (PDF), Video
- Lecture 4 (HTML), Lecture 4 (PDF), Lecture 4 notes (PDF), Video

### Lectures 5 and 6, 2019-09-24.

- Summary
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression. Lazy learning. K-NN. Kernel regression and kernel density estimation.
- Lecture 5 (HTML), Lecture 5 (PDF), Lecture 5 notes (PDF), Video
- Lecture 6 (HTML), Lecture 6 (PDF), Lecture 6 notes (PDF), Video

### Lectures 7 and 8, 2019-10-01.

- Summary
- Generative models: naive bayes, bayes. Comparing classifiers. Perceptron and Multilayer Perceptron.
- Lecture 7 (HTML), Lecture 7 (PDF), Lecture 7 notes (PDF), Video
- Lecture 8 (HTML), Lecture 8 (PDF), Lecture 8 notes (PDF), Video

### Lectures 9 and 10, 2019-10-08.

- Summary
- Maximum Margin Classifiers. Support vector machines for linear classification. SVM: soft margins, kernel trick, overfitting and regularization. Assignment 1.
- Lecture 9 (HTML), Lecture 9 (PDF), Lecture 9 notes (PDF), Video
- Lecture 10 (HTML), Lecture 10 (PDF), Lecture 10 notes (PDF), Video

### Lectures 11 and 12, 2019-10-15.

- Summary
- Multiclass classification. Bootstrapping. Bias-variance decomposition and tradeoff. Ensemble methods: bagging and boosting.
- Lecture 11 (HTML), Lecture 11 (PDF), Lecture 11 notes (PDF), Video
- Lecture 12 (HTML), Lecture 12 (PDF), Lecture 12 notes (PDF), Video

## Planned Classes. Please note that planned lectures are subject to changes; download notes and slides after the lecture.

### Lectures 13 and 14, 2019-10-22.

- Summary
- Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs.
- Lecture 13 (HTML)
- Lecture 14 (HTML)

### Lectures 15 and 16, 2019-10-29.

- Summary
- Introduction to unsupervised learning. Data visualization and feature selection. Dimensionality reduction: feature extraction with PCA; self-organzing maps.
- Lecture 15 (HTML)
- Lecture 16 (HTML)

### Revisions: 2019-11-05.

- Summary
- This session is reserved for student's questions and revisions.

### Lectures 17 and 18, 2019-11-12.

- Summary
- Introduction to clustering. K-means and k-medoids. Expectation maximization. Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation.
- Lecture 17 (HTML)
- Lecture 18 (HTML)

### Lectures 19 and 20, 2019-11-19.

- Summary
- Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Fuzzy sets and clustering. Fuzzy c-means. Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Second assignment.
- Lecture 19 (HTML)
- Lecture 20 (HTML)

### Lectures 21 and 22, 2019-11-26.

- Summary
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms. Deep learning. The problem of backpropagation. Autoencoders and Stacked Denoising Autoencoders.
- Lecture 21 (HTML)
- Lecture 22 (HTML)