Lectures

Week	Topic(s)	Slide(s)	Additional Resources
Week 01 13.02	Introduction to AI Methods and their Applications in Medicine	Lec 1: pdf	- https://www.youtube.com/watch?v=ll5LY7wI_Xc - https://www.youtube.com/watch?v=kjgFdfBYY9Q - https://www.youtube.com/watch?v=T-4pgL1rNAo - https://www.youtube.com/watch?v=iBWixWwn5Dk
Week 02 20.2	- Data & Data Types - Data Preprocessing - Example    - https://www.kaggle.com/code/alirezahasannejad/data-preprocessing-in-machine-learning	Lec 21: pdf Lec 22: pdf	- https://www.geeksforgeeks.org/ml-introduction-data-machine-learning/ - https://medium.com/datasciencearth/data-pre-processing-in-machine-learning-with-python-b07cd06866c7 - https://github.com/PacktPublishing/Hands-On-Data-Preprocessing-in-Python - https://github.com/vanya2v/Data-Cleaning-and-Preprocessing-CompFest15/blob/main/1_Data_Cleaning_COMPFEST.ipynb - https://github.com/karolinamgoma/Data-cleaning-and-preprocessing/blob/main/Live%20expectancy%20-%20data%20cleaning.ipynb - https://ml-course.github.io/master/notebooks/05%20-%20Data%20Preprocessing.html
Week 03 27.2	Supervised Learning - Basic Concepts - Decision Trees - Example : https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial6/tutorial6.html	Lec 31: pdf Lec 32: pdf	https://github.com/jakevdp/PythonDataScienceHandbook/blob/d66231454ef753818dc9213c9b5942e067266966/notebooks_v1/05.01-What-Is-Machine-Learning.ipynb https://github.com/jakevdp/PythonDataScienceHandbook/blob/d66231454ef753818dc9213c9b5942e067266966/notebooks_v1/05.08-Random-Forests.ipynb https://github.com/ageron/handson-ml3/blob/main/06_decision_trees.ipynb
Week 04 06.3	Supervised Learning - Model Evaluation and Evaluation Metrics - K-Nearest Neigbor - KNN Example   - https://www.kaggle.com/code/prashant111/knn-classifier-tutorial   - https://www.kaggle.com/code/shrutimechlearn/step-by-step-diabetes-classification	Lec 41: pdf Lec 42: pdf	https://www.geeksforgeeks.org/k-nearest-neighbor-algorithm-in-python/?ref=ml_lbp https://github.com/jakevdp/PythonDataScienceHandbook/blob/d66231454ef753818dc9213c9b5942e067266966/notebooks_v1/05.05-Naive-Bayes.ipynb
Week 05 13.3	Supervised Learning - Naive Bayes Classication - Naive Bayes Example   - https://www.kaggle.com/code/prashant111/naive-bayes-classifier-in-python    - https://www.kaggle.com/code/nisasoylu/naive-bayes-implementation-on-cancer-dataset - Artifical Neural Networks   - https://www.kaggle.com/code/saumandas/neural-networks-from-scratch-tutorial   - https://www.geeksforgeeks.org/classification-using-sklearn-multi-layer-perceptron/	Lec 51: pdf Lec 52: pdf	https://chriskhanhtran.github.io/minimal-portfolio/projects/breast-cancer.html https://python-course.eu/machine-learning/naive-bayes-classifier-with-scikit.php https://github.com/ageron/handson-ml3/blob/main/10_neural_nets_with_keras.ipynb https://www.pluralsight.com/resources/blog/guides/machine-learning-neural-networks-scikit-learn https://python-course.eu/machine-learning/neural-networks-with-scikit.php
Week 06 20.3	Supervised Learning - Support Vector Machines - Ensemble Methods - Overfiting Example for - SVM, Ensemble Learn, Overfitting :  https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial6/tutorial6.html	Lec 61 pdf Lec 62 pdf Lec 63 pdf	- https://github.com/jakevdp/PythonDataScienceHandbook/blob/d66231454ef753818dc9213c9b5942e067266966/notebooks_v1/05.07-Support-Vector-Machines.ipynb - https://github.com/ageron/handson-ml3/blob/main/07_ensemble_learning_and_random_forests.ipynb - https://github.com/jakevdp/PythonDataScienceHandbook/blob/d66231454ef753818dc9213c9b5942e067266966/notebooks_v1/05.03-Hyperparameters-and-Model-Validation.ipynb
	Midterm Week
Week 07 17.04	Supervised Learning - Regression - Examples   - https://www.kaggle.com/code/pratimborah/eda-linear-regression-us-medical-expenses   - https://ujangriswanto08.medium.com/analyzing-health-data-with-logistic-regression-in-python-592dfde2323d	Lec 71 pdf, pptx	- https://colab.research.google.com/drive/1tdJ30c0oBkW8TG6sjJzLL7-PYHCLhpef?usp=sharing - https://www.analyticsvidhya.com/blog/2021/06/application-of-machine-learning-in-medical-domain/ - https://colab.research.google.com/github/akashravichandran/jovian-zerotogan/blob/master/02_insurance_linear_regression.ipynb - https://github.com/pptr3/personal-blog/blob/master/article/logistic-regression/jupyter-notebook/LogisticRegression.ipynb
Week 08 24.04	Unsupervised Learning - Cluster Analysis and Paritional Algorithms - Examples   - https://sonraianalytics.com/k-means-clustering-and-data-mining-in-precision-medicine/   - https://www.kaggle.com/code/datark1/customers-clustering-k-means-dbscan-and-ap	Lec 81 pdf, pptx	- https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html - https://github.com/jakevdp/sklearn_pycon2015/blob/master/notebooks/04.2-Clustering-KMeans.ipynb - https://realpython.com/k-means-clustering-python/ - https://github.com/ali-hazan/jupyter-play-ground/blob/main/Computer%20Vision/Image_segmentation.ipynb
Week 09 01.05	Labor and Solidarity Day
Week 10 08.05	Unsupervised Learning - Clustering Other Methods Examples - https://www.kaggle.com/code/darkaster/clustering-heart-disease-patient-data Anomaly Detection Examples  - https://www.kaggle.com/code/sagnik1511/eeg-data-analysis-anomaly-searching - https://www.kaggle.com/code/yorkyong/exploring-eeg-a-beginner-s-guide	Lec 91 pdf, pptx Lec 92 pdf, pptx	- https://medium.com/analytics-vidhya/identifying-relationships-in-clinical-text-nlp-clustering-929eb04b5942 - https://actuariesinstitute.github.io/cookbook/docs/DAA_M06_Ex5.html - https://www.kaggle.com/code/chandrimad31/clustering-with-pca-kmeans-hierarchical-dbscan - https://github.com/gouravaich/density-based-clustering-dbscan/blob/master/DBSCAN%20Notebook.ipynb - https://www.kaggle.com/code/praxitelisk/anomaly-detection-techniques-summary/code  - https://www.kaggle.com/code/drscarlat/medical-claims-anomaly-detection - https://github.com/datasciencecampus/anomaly-detection/blob/master/Anomaly_detection.ipynb - https://medium.com/data-science/introduction-to-anomaly-detection-in-python-with-pycaret-2fecd7144f87 - https://www.datature.io/blog/leveraging-datature-nexus-for-tumor-and-anomaly-detection-in-medical-scans - https://paperswithcode.com/task/anomaly-detection - https://unit8.com/resources/anomaly-detection-in-healthcare-data-with-darts/ - https://www.datacamp.com/tutorial/introduction-to-anomaly-detection - https://www.kaggle.com/code/harshsingh2209/anomaly-detection-with-autoencoders - https://www.kaggle.com/discussions/general/128356 - https://run.unl.pt/bitstream/10362/132860/1/TCDMAA0120.pdf
			- https://www.kaggle.com/code/writetoneeraj/fundamentals-of-medical-image-processing

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