Advanced Analytics for a Big Data World

# Advanced Analytics for a Big Data World (2026) This page contains slides, references and materials for the "Advanced Analytics for a Big Data World" course. *Last updated at 2026-03-30* # Table of contents

# Course 1: February 9 ## Slides * [About the Course](./slides/00-About.pdf) * [Introduction](./slides/01-Introduction.pdf) ## Assignment Information The evaluation of this course consists of a lab report (50% of the marks) and a closed-book written exam with both multiple-choice and open questions (50% of the marks). * Your lab-report will consist of your write-ups of four assignments, which will be made available throughout the semester * You will work in groups of five students * The four assignments consist of (1) Predictive model competition using R or Python (tabular); (2) Deep learning application (imagery); (3) Text mining with generative AI (text); (4) Social network/graph analytics assignment (network) * Per assignment, you describe your results (screen shots, numbers, approach); more detailed information will be provided per assignment * You do not hand in each assignment separately, but hand in your completed lab report containing all four assignments on Sunday May 31st **For forming groups, please see the Toledo page after the first course.** **Note for externals (i.e. anyone who will NOT partake in the exams -- this doesn't apply to normal students)**: you are free to partake in (any of) the assignments individually (they'll be posted on this page as well), but not required to. ## Recording * [YouTube](https://youtu.be/YObDLUDqORo) ## Background Information Extra references: * [Deepmind blog](https://deepmind.google/blog/) * [OpenAI blog](https://openai.com/index/) * [Anthropic research blog](https://www.anthropic.com/research/) * [Qwen Coder Next](https://qwen.ai/blog?id=qwen3-coder-next) * [Kimi K2](https://moonshotai.github.io/Kimi-K2/thinking.html) * [Your brain on ChatGPT](https://www.media.mit.edu/publications/your-brain-on-chatgpt/) * [AlphaGo](https://www.engadget.com/2016-03-14-the-final-lee-sedol-vs-alphago-match-is-about-to-start.html) and their newer ["AlphaGo Zero"](https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/) * [AlphaStar](https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/) * [AlphaFold](https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery) and a [recent article where it was used](https://arxiv.org/abs/2201.09647) * [AlphaCode](https://alphacode.deepmind.com/) * [DALL·E: Creating Images from Text](https://openai.com/blog/dall-e/) * [Automating My Job with GPT-3](https://blog.seekwell.io/gpt3) * [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) * [ChatGPT](https://openai.com/blog/chatgpt/) * [Millions of new materials discovered with deep learning](https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/) * [AlphaGeometry](https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/) * [DeepSeek expained](https://heidloff.net/article/deepseek-r1/) and [announcement](https://github.com/deepseek-ai/DeepSeek-R1) * [Janus Pro is DeepSeek's image generator](https://huggingface.co/deepseek-ai/Janus-Pro-7B) * [Kimi](https://github.com/MoonshotAI/Kimi-k1.5) and [Qwen](https://github.com/QwenLM/Qwen) * [Hunyuan3D-2](https://github.com/Tencent/Hunyuan3D-2) * [The Economics of AI Today](https://thegradient.pub/the-economics-of-ai-today/) * [Designing great data products: The Drivetrain Approach: A four-step process for building data products](https://www.oreilly.com/radar/drivetrain-approach-data-products/) * [Google's Rules of ML](https://developers.google.com/machine-learning/guides/rules-of-ml) * [150 successful machine learning models: 6 lessons learned at Booking.com](https://blog.acolyer.org/2019/10/07/150-successful-machine-learning-models/) -- recommended read! * The tank story: [how much is true?](https://www.gwern.net/Tanks) * [Self-driven car spins in circles](https://twitter.com/mat_kelcey/status/886101319559335936) * [Correlation is not causation](https://web.archive.org/web/20210413060837/http://robertmatthews.org/wp-content/uploads/2016/03/RM-storks-paper.pdf) * [Once billed as a revolution in medicine, IBM’s Watson Health is sold off in parts](https://www.statnews.com/2022/01/21/ibm-watson-health-sale-equity/) * [How To Break Anonymity of the Netflix Prize Dataset](https://arxiv.org/abs/cs/0610105) * [Why UPS drivers don’t turn left and you probably shouldn’t either](http://www.independent.co.uk/news/science/why-ups-drivers-don-t-turn-left-and-you-probably-shouldn-t-either-a7541241.html) # Course 2: February 16 ## Slides * [Supervised Essentials I](./slides/02-Supervised-1.pdf) ## Recording * [YouTube](https://youtu.be/BFi-FQGL4s8) ## Background Information [In the news](./news/News_02-16.pptx) (slides) Extra references on preprocessing: * [Forecasting with Google Trends](https://medium.com/dataminingapps-articles/forecasting-with-google-trends-114ab741bda4) * [Google Street View in insurance](https://arxiv.org/ftp/arxiv/papers/1904/1904.05270.pdf) * [Predicting the State of a House Using Google Street View](https://link.springer.com/chapter/10.1007/978-3-031-05760-1_46) * Packages for missing value summarization: [missingno](https://github.com/ResidentMario/missingno) and [VIM](https://cran.r-project.org/web/packages/VIM/index.html) * [MICE is also a popular package for dealing with missing values in R](https://www.r-bloggers.com/imputing-missing-data-with-r-mice-package/) * [More on data "leakage" and why you should avoid it](https://www.kaggle.com/alexisbcook/data-leakage) * [Another excellent presentation on the types of data leakage](https://www.slideshare.net/YuriyGuts/target-leakage-in-machine-learning) * [`smbinning`, an R package for weights of evidence encoding](https://cran.r-project.org/web/packages/smbinning/index.html) * [`category_encoders`: an interesting package containing a wide variety of categorical encoding techniques](http://contrib.scikit-learn.org/category_encoders/) * [More on the leave one out mean](https://www.kaggle.com/c/caterpillar-tube-pricing/discussion/15748) as discussed on Kaggle * [More explanation on the hashing trick on Wikipedia](https://en.wikipedia.org/wiki/Feature_hashing) * [Feature Hashing in Python](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html) * [Entity Embeddings of Categorical Variables](https://arxiv.org/pdf/1604.06737.pdf) * [`dm` R package](https://krlmlr.github.io/dm/) * [`skrub`: Prepping tables for machine learning](https://github.com/skrub-data/skrub) * [`featuretools`: An open source python framework for automated feature engineering](https://www.featuretools.com/) * See also [`stumpy`](https://github.com/TDAmeritrade/stumpy) and [`tsfresh`](https://tsfresh.readthedocs.io/) * [AutoFeat](https://github.com/cod3licious/autofeat) * [FeatureSelector](https://github.com/WillKoehrsen/feature-selector) * [OneBM](https://arxiv.org/abs/1706.00327) * [More information on principal component analysis (PCA)](http://setosa.io/ev/principal-component-analysis/) * [OpenCV](http://opencv.org/) (for feature extraction from facial images), or see [this page](https://github.com/ageitgey/face_recognition) * Interesting application of PCA to "understand" the latent features of a deep learning network: [https://www.youtube.com/watch?v=4VAkrUNLKSo](https://www.youtube.com/watch?v=4VAkrUNLKSo) * [Another application of PCA for understanding model outputs](https://github.com/asabuncuoglu13/sketch-embeddings) Extra references on supervised basics: * [“Building Bridges between Regression, Clustering, and Classification”](https://arxiv.org/abs/2502.02996) * [Frank Harell on stepwise regression](https://www.stata.com/support/faqs/statistics/stepwise-regression-problems/) * [Stepwise feature selection in scikit-learn](https://scikit-learn.org/stable/modules/feature_selection.html#sequential-feature-selection) but note that it applies cross validation * [L1 and L2 animation](https://nitter.net/itayevron/status/1328421322821693441) * [aerosolve - Machine learning for humans](https://medium.com/airbnb-engineering/aerosolve-machine-learning-for-humans-55efcf602665) * [ID3.pdf](./papers/ID3.pdf) and [C45.pdf](./papers/C45.pdf): extra material regarding decision trees * [Nice video on Entropy and Information](https://www.youtube.com/watch?v=v68zYyaEmEA) * [CloudForest](https://github.com/ryanbressler/CloudForest), an older but interesting decision tree ensemble implementation with support for three-way splits to deal with missing values, implemented in... Go * [RIPPER](https://christophm.github.io/interpretable-ml-book/rules.html), [RuleFit](https://github.com/christophM/rulefit) and [Skope-Rules](https://github.com/scikit-learn-contrib/skope-rules) * [dtreeviz](https://github.com/parrt/dtreeviz) for nicer visualizations or [pybaobabdt](https://pypi.org/project/pybaobabdt/) * White box models can be easily deployed, even in Excel... For some fun examples, see [m2cgen](https://github.com/BayesWitnesses/m2cgen), which can convert ML models to Java, C, Python, Go, PHP, ..., [emlearn](https://github.com/emlearn/emlearn) converts ML code to portable C99 code for microcontrollers, [sklearn-porter](https://github.com/nok/sklearn-porter) converts scikit-learn models to C, Java and others, and [SKompiler](https://github.com/konstantint/SKompiler) converts scikit-learn models to SQL queries, and... Excel * Two newer examples using k-NN: [1](https://towardsdatascience.com/scanned-digits-recognition-using-k-nearest-neighbor-k-nn-d1a1528f0dea) and [2](https://medium.com/learning-machine-learning/recommending-animes-using-nearest-neighbors-61320a1a5934) # Course 3: February 23 ## Slides * [Supervised Essentials II](./slides/03-Supervised-2.pdf) ## Recording * [YouTube](https://youtu.be/8uZy_75YHv4) ## Background Information [In the news](./news/News_02-23.pptx) (slides) Extra references: * [ROSE](https://cran.r-project.org/web/packages/ROSE/index.html) is a popular package for dealing with over/undersampling in R * [imblearn](https://imbalanced-learn.org/stable/) contains many smart sampling implementations for Python * [Tuning Imbalanced Learning Sampling Approaches](https://www.dataminingapps.com/2019/06/tuning-imbalanced-learning-sampling-approaches/) * More on the ROC curve: [1](https://arxiv.org/pdf/1812.01388.pdf), [2](http://www.rduin.nl/presentations/ROC%20Tutorial%20Peter%20Flach/ROCtutorialPartI.pdf), [3](https://stats.stackexchange.com/questions/225210/accuracy-vs-area-under-the-roc-curve/225221#225221) * [Averaging ROC curves for multiclass](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html) * [The Relationship Between Precision-Recall and ROC Curves](./papers/rocpr.pdf): a paper by KU Leuven's Jesse Davis et al. on the topic with some other interesting remarks * [h-index.pdf](./papers/h-index.pdf): paper regarding the h-index as an alternative for AUC * [BSZ tuning](./papers/bsztuning.pdf): paper on BSZ tuning: a simple cost-sensitive regression approach * [A blog post explaining cross validation](https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6) * [Multiclass and multilabel algorithms in scikit-learn](https://scikit-learn.org/stable/modules/multiclass.html) * [scikit.ml](http://scikit.ml/) contains more advanced multilabel techniques * More on probability calibration [here](http://scikit-learn.org/stable/modules/calibration.html) and [here](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression) * [More on the System Stability Index](https://www.dataminingapps.com/2016/10/what-is-a-system-stability-index-ssi-and-how-can-it-be-used-to-monitor-population-stability/) * [Visibility and Monitoring for Machine Learning Models](http://blog.launchdarkly.com/visibility-and-monitoring-for-machine-learning-models/) * [What's your ML test score? A rubric for production ML systems](https://research.google.com/pubs/pub45742.html) * [Hidden Technical Debt in Machine Learning Systems](https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems) ## Assignment 1 In this assignment, you will construct a predictive model to predict the future revenue of customers of a European fashion shop. The data set is centered around online (web shop) sales of shoes. The data set can be downloaded through Toledo. Student groups will play "competitively" using [this website](https://seppe.net/aa/assignment1/) * **You will have received a password and your final group number through an email, which you need to make submissions**; in case of issues, contact me as soon as possible * The test set does not contain the target, so you will need to split up the train set accordingly to create your own validation set. The test set supplied in the data is used to rank and assess your model on the competition leaderboard * Your model will be evaluated using two metrics: Mean Absolute Error and Spearman Correlation as a secondary score * Note that only about half of the test set is used for the "public" leaderboard. That means that the score you will see on the leaderboard is done using this part of the test only (you don't know which half). Later on through the semester, submissions are frozen and the resuls on the "hidden" part will be revealed * Outliers, noisy data, missing values, and the heavy featurization you will have to perform will make for challenges to be overcome * The results of your latest submission are used to rank you on the leaderboard. This means it is your job to keep track of different model versions / approaches / outputs in case you'd like to go back to an earlier result * Once the hidden leaderboard is revealed, you should reflect on both results and explain accordingly in your report. E.g. if you did well on the public leaderboard but not on the hidden one, what might have caused this? The idea is not that you then step in and "fix" your model, but to learn and reflect * Also, whilst you can definitely try, the goal is not to "win", but to help you reflect on your model's results, see how others are doing, etc. * Your model needs to be build using Python (or R, Go, Rust, Julia or whatever you prefer as long as it involves coding). As an environment, you can use e.g. Jupyter (Notebook or Lab), RStudio, Google Colaboratory, Microsoft Azure Machine Learning Studio... and any additional library or package you want * Feel free to use ChatGPT, but try to make sure you know what you are doing **Data** Note that the data set contains the following files: - `transactions.csv`: transactions (purchases) during the feature construction period (y1-y2). Note that the same customer can obviously have made multiple purchases. You will need to use this to construct informative features - `customer_clv_train.csv`: revenue for each train set customer during the target observation period (y3-y4) - `customer_clv_test.csv`: the test customers you need to predict the revenue for during the same target observation period The train/test split was made out of sample instead of out of time. This is not ideal but was the only option as not enough data was available to construct transactions and targets over a longer time span. The data set was already minimally pre-processed and anonymized where necessary, but probably a lot of processing remains. Tip: first perform a thorough exploration of the data set. You will note that there is a large group of customers that do not generate any revenue during the target observation period, meaning that they shopped in y1-y2, but did not come back. Think how you can incorporate this effectively in your modelling pipeline. The features for each transaction are as follows: - `cust_id`: unique identifier of the customer placing the order - `order_date`: date when the order was placed - `pack_date`: date when the order was packed/shipped - `sale_id`: unique identifier of the sales transaction (multiple articles can be present in the same transaction) - `sale_discount_applied`: monetary value of discount applied to the sale - `sale_revenue`: final revenue amount received for this line item after discount - `returned_to_shop_id`: identifier of the shop/location where the item was returned (empty if not returned) - `prod_id`: unique identifier of the purchased product - `prod_size`: shoe size of the product - `prod_web_only`: binary flag (1/0) indicating whether the product is sold online only - `prod_season`: season or collection code (e.g., W14 = Winter 2014) - `prod_brand`: brand name of the product - `prod_title`: full commercial product name/title - `prod_color`: primary color of the product - `prod_type_1`: primary target group or category (e.g., men, women, boys) - `prod_type_2`: not included (= "shoes") - `prod_type_3`: secondary product category (e.g., sneakers, high shoes) - `prod_type_4`: tertiary style classification (e.g., high-top sneakers) - `prod_type_5`: additional style descriptor (e.g., boots with velcro, dress boots) - `prod_heel`: heel type or heel specification (if known) - `prod_material`: main outer material of the shoe (e.g., leather, suede) (if known) - `prod_insole`: indicator of specific insole feature (if known) - `prod_print`: type of print or pattern (if known) - `prod_comfort_sole`: indicator of special comfort sole feature (if known) - `prod_comfort_wear`: indicator of enhanced comfort wear feature (if known) - `prod_clasp`: type of closing mechanism (e.g., velcro, zipper, lace-up) (if known) - `prod_outlet`: indicator how often this product was sold through an outlet channel, higher values indicate that the product appeared more often **Deliverables** * Feel free to include code fragments, tables, visualisations, etc. * Some groups prefer to write their final report using Jupyter Notebook, which is fine, as long as it is readable top-to-bottom * You can use any predictive technique/approach you want, though focus on the whole process: general setup, critical thinking, and the ability to get and validate an outcome * You're free to use unsupervised technique for your data exploration part, too. When you decide to build a black box model, including some interpretability techniques to explain it is a nice idea * Any other assumptions or insights are thoughts can be included as well: the idea is to take what we've seen in class, get your hands dirty and try out what we've seen **Important: All groups should submit the results of their predictive model at least once to the leaderboard before the hidden scores are revealed (I'll warn you in time)** More info on how to submit can be found on the [submission website](https://seppe.net/aa/assignment1/). *You do not hand in each assignment separately, but hand in your completed lab report containing all four assignments on Sunday May 31st. For an overview of the groups, see Toledo. Note for externals (i.e. anyone who will NOT partake in the exams -- this doesn't apply to normal students): you are free to partake in (any of) the assignments individually, but not required to.* # Course 4: March 2 ## Slides * Same as last week ## Recording * [YouTube](https://youtu.be/Xf-LtVoivNM) ## Background Information [In the news](./news/News_03-02.pptx) (slides) Extra references on interpretability: * [Fantastic book on the topic of interpretability](https://christophm.github.io/interpretable-ml-book/) * [http://explained.ai/rf-importance/index.html](Beware of using feature importance!) * [https://academic.oup.com/bioinformatics/article/26/10/1340/193348](Permutation importance: a corrected feature importance measure) * [Interpreting random forests: Decision path gathering](http://blog.datadive.net/interpreting-random-forests/) * [Local interpretable model-agnostic explanations](https://github.com/marcotcr/lime) * [SHAP (SHapley Additive exPlanations)](https://github.com/slundberg/shap) * [Another great overview](https://github.com/jphall663/awesome-machine-learninginterpretability) * [rfpimp package](https://pypi.org/project/rfpimp/) * [Forest floor](http://forestfloor.dk/) for higher-dimensional partial depence plots * [The pdp R package](https://cran.r-project.org/web/packages/pdp/pdp.pdf) * [The iml R Package](https://cran.r-project.org/web/packages/iml/index.html) * [Descriptive mAchine Learning EXplanations (DALEX) R Package](https://github.com/pbiecek/DALEX) * [eli5 for Python](https://eli5.readthedocs.io/en/latest/index.html) * [Skater for Python](https://github.com/datascienceinc/Skater) * scikit-learn has Gini-reduction based importance but permutation importance has [been added in recent versions](https://scikit-learn.org/stable/modules/permutation_importance.html) * Or with [https://github.com/parrt/random-forest-importances](https://github.com/parrt/random-forest-importances) * Or with [https://github.com/ralphhaygood/sklearn-gbmi](https://github.com/ralphhaygood/sklearn-gbmi) (sklearn-gbmi) * [pdpbox for Python](https://github.com/SauceCat/PDPbox) * [vip for Python (and R)](https://koalaverse.github.io/vip/index.html) * [https://medium.com/@Zelros/a-brief-history-of-machine-learning-models-explainability-f1c3301be9dc](https://medium.com/@Zelros/a-brief-history-of-machine-learning-models-explainability-f1c3301be9dc) * Graft, Reassemble, Answer delta, Neighbour sensitivity, Training delta (GRANT) - [https://github.com/wagtaillabs](https://github.com/wagtaillabs) * [Classification Acceleration via Merging Decision Trees](./papers/mergingtrees.pdf) # Course 5: March 9 ## Slides * [Supervised Essentials III](./slides/04-Supervised-3.pdf) * [Deep Learning I](./slides/05-DeepLearning-1.pdf) ## Recording * [YouTube](https://youtu.be/s5gitU4dQb8) ## Background Information [In the news](./news/News_03-09.pptx) (slides) Extra references on ensemble models: * The [jar of jelly beans](https://towardsdatascience.com/the-unexpected-lesson-within-a-jelly-bean-jar-1b6de9c40cca) * The [documentation of scikit-learn](http://scikit-learn.org/stable/modules/ensemble.html) is very complete in terms of ensemble modeling * Kaggle post on [model stacking](http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/) * [Random forest.pdf](./papers/Random forest.pdf): the original paper on random forests * [ExtraTrees](https://scikit-learn.org/stable/modules/ensemble.html#forest): "In extremely randomized trees (see ExtraTreesClassifier and ExtraTreesRegressor classes), randomness goes one step further in the way splits are computed. As in random forests, a random subset of candidate features is used, but instead of looking for the most discriminative thresholds, thresholds are drawn at random for each candidate feature and the best of these randomly-generated thresholds is picked as the splitting rule" * Also interesting to note is that scikit-learn's implementation of decision trees (and random forest) supports [multi-output problems](https://scikit-learn.org/stable/modules/tree.html#tree-multioutput) * Note that some implementations/papers for ExtraTrees will go a step further and simply select a splitting point completely at random (e.g. the subset of thresholds is size 1 -- this is helpful when working with very noisy features) * [To tune or not to tune the number of trees in a random forest](./papers/tune_or_not.pdf); conclusions: use a sufficiently high amount of trees * [Adaboost.pdf](./papers/Adaboost.pdf): the original paper on AdaBoost * [alr.pdf](./papers/alr.pdf): Friedman's paper on AdaBoost and Additive Logistic Regression * [xgboost documentation](https://xgboost.readthedocs.io/en/latest/) with a good [introduction](https://xgboost.readthedocs.io/en/latest/tutorials/model.html) * [lightgbm documentation](https://lightgbm.readthedocs.io/en/latest/pythonapi/) * [catboost documentation](https://catboost.ai/en/docs/) * Note that all three of these have sklearn-API compatible classifiers and regressors, so you can combine them with other typical sklearn steps # Course 6: March 16 ## Slides * Continuing with the slides of last time ## Recording * [YouTube](https://youtu.be/WZZSWOHXaO0) ## Background Information [In the news](./news/News_03-16.pptx) (slides) Extra references: * [Keras Vision tutorials - use these to get started with assignment 2!](https://keras.io/examples/vision/) * [A brief history of AI](https://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.html) * [Who invented the reverse mode of differentiation](https://www.math.uni-bielefeld.de/documenta/vol-ismp/52_griewank-andreas-b.pdf) * [Backpropagation explained](http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html) * [Great short YouTube playlist explaining ANNs (3blue1brown)](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) * [And other one explaining convolutions (3blue1brown)](https://www.youtube.com/watch?v=8rrHTtUzyZA ) * [Introduction to neural networks](https://victorzhou.com/blog/intro-to-neural-networks/) * [Tensorflow playground](https://playground.tensorflow.org/) * [NeuroEvolution of Augmenting Topologies (NEAT)](https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies) * In class, I mentioned some recent papers around the bias-variance double descent phenomenon. [Chatterjee, S., & Zielinski, P. (2022). On the generalization mystery in deep learning](https://arxiv.org/abs/2203.10036) and especially [Wilson, A. G. (2025). Deep Learning is Not So Mysterious or Different](https://arxiv.org/abs/2503.02113) are interesting to read ## Assignment 2 In this assignment, you will construct a deep learning model to predict a target on a data set of Lego Minifigs (mini figures). The data was obtained from [brickset.com](https://brickset.com). **Data** * The set of images can be downloaded [using this link](https://e.pcloud.link/publink/show?code=XZYQmGZwIUEyLDBKRmnOsRuq4zub5BpRs2X) * You will also need [this json file](./assignment2/minifigs.json) containing the metadata The JSON file has an entry for each image looking e.g. like this: ``` { "id": 2, "name": "LEGOLAND - Black Torso, Black Legs, Black Hat", "link": "/minifigs/old011/legoland-black-torso-black-legs-black-hat", "year": 1975, "img_url": "https://img.bricklink.com/ItemImage/MN/0/old011.png", "minifig_number": "OLD011", "category": "LEGOLAND", "subcategory": "General", "year_released": "1975", "set_id": "7 sets", "current_value_new": "Not known", "current_value_used": "~\u20ac1.53", "character_name": null, "img_local_path": "images/OLD011.jpg", "themes": ["Basic", "LEGOLAND", "Universal Building Set"] }, ``` Note: the `\u20ac` stands for the EUR currency sign. **Objective** Your task is to construct a deep learning model to predict one of the following (you can choose what you find most interesting to work on): * Multiclass: try to predict the category based on the image * Multilabel: try to predict which "themes" are present (a bit harder) * Regression: try to predict the year of release, or the "value" of the mini figure (this is probably harder to do but can also be fun) If you find the classes or labels too large, you can also decide to focus on the top-k most occurring ones instead. Split the data set into train / val / test sets in a careful manner. Pick an evaluation metric to report on in accordance with the chosen task. **Tips** * You can train a model from scratch, but fine-tuning an existing image is probably a good idea * Try using an interpretability technique to figure out what your model is focusing on * Check the tutorials on [https://keras.io/examples/vision/](https://keras.io/examples/vision/) * You can use any deep learning library you want, but Keras or PyTorch will probably be easiest **Deliverables** * Overview of your full pipe line, including architecture, trade-offs, ways used to prevent overfitting, etc. * Results based on your chosen evaluation metric * Illustration of your model's predictions on a couple of test images *You do not hand in each assignment separately, but hand in your completed lab report containing all four assignments on Sunday May 31st. For an overview of the groups, see Toledo. Note for externals (i.e. anyone who will NOT partake in the exams -- this doesn't apply to normal students): you are free to partake in (any of) the assignments individually, but not required to.* # Course 7: March 23 ## Slides * Continuing with the slides of last time ## Recording * [YouTube](https://youtu.be/6kjVUeeJs5k) ## Background Information [In the news](./news/News_03-23.pptx) (slides) # Course 8: March 30 ## Slides * [Unsupervised Learning](./slides/06-Unsupervised.pdf) ## Recording * [YouTube](https://youtu.be/roLJn0QUuRE) ## Background Information [In the news](./news/News_03-30.pptx) (slides) Extra references: * [Comparing different hierarchical linkage methods on toy datasets](https://scikit-learn.org/stable/auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py) * [Visualisation of the DBSCAN clustering technique in the browser](https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/) * [More on the Gower distance](https://medium.com/@rumman1988/clustering-categorical-and-numerical-datatype-using-gower-distance-ab89b3aa90d9) * [Self-Organising Maps for Customer Segmentation using R](https://www.r-bloggers.com/self-organising-maps-for-customer-segmentation-using-r/) * [t-SNE](https://lvdmaaten.github.io/tsne/) and using it for [anomaly detection](https://medium.com/@Zelros/anomaly-detection-with-t-sne-211857b1cd00) * [http://distill.pub/2016/misread-tsne/](http://distill.pub/2016/misread-tsne/) provides very interesting visualisations and more explanation on t-SNE * [Be careful when clustering the output of t-SNE](https://stats.stackexchange.com/questions/263539/clustering-on-the-output-of-t-sne/264647#264647) * [UMAP](https://umap-learn.readthedocs.io/en/latest/how_umap_works.html) * [PixPlot](https://dhlab.yale.edu/projects/pixplot/): another cool example of t-SNE (this was the name I was trying to recall during class) * [Isolation forests](https://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py) * [Local outlier factor](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-outlier-detection-py) * [Twitter's anomaly detection package](https://github.com/twitter/AnomalyDetection) and [prophet](https://facebook.github.io/) * [An interesting article on detecting NBA all-stars using CADE](http://darrkj.github.io/blog/2014/may102014/) * Papers on [DBSCAN](./papers/dbscan.pdf), [isolation forests](./papers/iforest.pdf) and [CADE](./papers/CADE.pdf) # Course 9: April 20 ## Slides * [Deep Learning II](./slides/07-DeepLearning-2.pdf) # Resources ## Books If you want an exhaustive list of data science books (not required for the course), feel free to check out: - [https://github.com/MasoudKaviani/freemachinelearninigbooks](https://github.com/MasoudKaviani/freemachinelearninigbooks) - [https://github.com/chaconnewu/free-data-science-books](https://github.com/chaconnewu/free-data-science-books) - [https://github.com/bradleyboehmke/data-science-learning-resources](https://github.com/bradleyboehmke/data-science-learning-resources) - [https://github.com/Saurav6789/Books-](https://github.com/Saurav6789/Books-) - [https://github.com/yashnarkhede/Data-Scientist-Books](https://github.com/yashnarkhede/Data-Scientist-Books) ## Python Tutorials Python itself is [quite easy](https://learnxinyminutes.com/docs/python/); you mainly need to figure out the additional libraries and their usage. Try to become familiar with `Numpy`, `Pandas`, and `scikit-learn` first, e.g. [play along with a couple of these examples](https://scikit-learn.org/stable/auto_examples/index.html). The bottom of [this page](https://learnxinyminutes.com/docs/python/) also lists some more resources to learn Python. The following are quite good: * [A Crash Course in Python for Scientists](https://nbviewer.jupyter.org/gist/anonymous/5924718) * [Dive Into Python 3](https://diveintopython3.net/index.html) * [https://docs.python-guide.org/](https://docs.python-guide.org/) (a bit more intermediate) * Someone has also posted [this 100 Page Python Intro](https://learnbyexample.github.io/100_page_python_intro/introduction.html) ## DataCamp To get access to DataCamp, use this [registration link](https://www.datacamp.com/groups/shared_links/67256766fea5a442a7217822450f1fd5d83a7f46711f8a90f06348f5c1de0d61). You can use this classroom to get access to courses to enhance your learning. Note that this will require a @(student.)kuleuven.be email address. If you'd like to use a personal email instead (e.g. because you already have an account on DataCamp), send me an email.