Gregor Balkovec <gregor.balkovec@ltfe.org
>
Anže Glušič <anze.glusic@ltfe.org
>
Zaporedje | Datum | Dan | Tip |
---|---|---|---|
1. | 2.4.2024 | Tor | Predavanje 1 |
2. | 9.4.2024 | Tor | Predavanje 2 |
3. | 16.4.2024 | Tor | Predavanje 3 |
4. | 23.4.2024 | Tor | Predavanje 4 |
/ | / | ||
5. | 6.5.2024, 16:30, ZOOM | PON | Predavanje 5 |
/. | / | ||
6. | 14.5.2024 | Tor | Predavanje 6 |
7. | 21.5.2024 | Tor | Predavanje 7 |
8. | 28.5.2024 | Tor | Predavanje 8 |
9. | 4.6.2024 | Tor | Predavanje 9 |
10. | 11.6.2024 | Tor | Predavanje 10 |
11. | po dogovoru | Tor | Izpit |
- Teoretičen uvod v strojno učenje ✅
- Workflow of a machine learning project ✅
- What is machine learning? ✅
- What are machine learning models? ✅
- Why Machine Learning? ✅
- Problems Machine Learning Can Solve ✅
- scikit-learn ✅
- A First Application: Classifying Iris Species ✅
- Uvod v nadzorovano učenje ✅
- Linear models for regression ✅
- Feature scaling ✅
- Regularization ✅
- Polynomial regression ✅
- Linear models for classification ✅
- Example: North American pumpkin prices ✅
- k-Nearest Neighbors ✅
- Naive Bayes Classifiers ✅
- Kernelized Support Vector Machines ✅
- Decision Trees ✅
- Vaja: Phone prices ✅
- Intro to Feature Engineering ✅
- Foreseeing Variable Problems When Building ML Models ✅
- Missing data imputation ✅
- Encoding Categorical Variables ✅
- Transforming Numerical Variables ✅
- Variable Discretization ✅
- Handling outliers ✅
- Creating features from date and time ✅
- Working with latitudes and longitudes ✅
- Cross-Validation ✅
- Grid Search ✅
- Hyperparameter Optimization ✅
- Evaluation Metrics and Scoring ✅
- Automatic Feature Selection ✅
- Intro To Pipelines ✅
- Example: Pipelines usage ✅
- Introduction to Ensemble Learning ✅
- Ensembles of Decision Trees ✅
- XGBoost ✅
- Recommender systems ✅
- Recommender systems Exercise ✅
- Uvod v nenadzorovano učenje ✅
- Clustering ✅
- Dimension Reduction ✅
- Intro to Time Series Forecasting ✅
- Understanding time series forecasting ✅
- Modeling a moving average process ✅
- Modeling an autoregressive process ✅
- Modeling complex time series ✅
- Forecasting non-stationary time series ✅
- Accounting for seasonality ✅
- Adding external variables to models ✅
- End-to-End Machine Learning Project
- Overview of Machine Learning