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school of AI

Introductory course in Machine Learning with lecture notebooks and hands-on workshop materials.

Authors

Levan Tsinadze - https://www.linkedin.com/in/levan-tsinadze-246a1b2a/

Anzor Gozalishvili - https://www.linkedin.com/in/anzor-gozalishvili

Sandro Barnabishvili - https://www.linkedin.com/in/sandrobarna

Course Syllabus

Course Outcome

After successful completion of this course, students will have good understanding of fundamental Machine Learning algorithms and will be able to solve intermediate level real world problems on their own.

Difficulty

Intermediate level

General instructions

By cloning the repo, you will have the notebooks needed during the course. All these notebooks can be run using Google Colab (Ready to go)

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school of AI

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Course Syllabus

Course Outcome

Difficulty

General instructions

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Name		Name	Last commit message	Last commit date
Latest commit History 210 Commits
configs		configs
data		data
homework		homework
images		images
install		install
maxin_ai_meetings		maxin_ai_meetings
maxinai		maxinai
samples		samples
seminars		seminars
terminology		terminology
tests		tests
utils		utils
.gitignore		.gitignore
README.md		README.md
additional_1_representation_learning.ipynb		additional_1_representation_learning.ipynb
additional_2_self_attention.ipynb		additional_2_self_attention.ipynb
additional_self-supervised_representation_learning_3_part.ipynb		additional_self-supervised_representation_learning_3_part.ipynb
additional_seminar_optimizations.ipynb		additional_seminar_optimizations.ipynb
additional_workshop_representation_learning.ipynb		additional_workshop_representation_learning.ipynb
additional_workshop_representation_learning_and_search.ipynb		additional_workshop_representation_learning_and_search.ipynb
dert_transformer_based_object_detection.ipynb		dert_transformer_based_object_detection.ipynb
intro_part_2.ipynb		intro_part_2.ipynb
learning_and_relational_spaces.ipynb		learning_and_relational_spaces.ipynb
lecture_10_naive_bayes.ipynb		lecture_10_naive_bayes.ipynb
lecture_11_trees_and_ensembles.ipynb		lecture_11_trees_and_ensembles.ipynb
lecture_12_dnn.ipynb		lecture_12_dnn.ipynb
lecture_13_unsupervised_ml.ipynb		lecture_13_unsupervised_ml.ipynb
lecture_14_optimizations.ipynb		lecture_14_optimizations.ipynb
lecture_15_regularizations.ipynb		lecture_15_regularizations.ipynb
lecture_16_convolutional_neural_networks.ipynb		lecture_16_convolutional_neural_networks.ipynb
lecture_17_backpropagation_and_convolution.ipynb		lecture_17_backpropagation_and_convolution.ipynb
lecture_18_segmentation_detection.ipynb		lecture_18_segmentation_detection.ipynb
lecture_19_object-detection.ipynb		lecture_19_object-detection.ipynb
lecture_1_linear_algebra_1.ipynb		lecture_1_linear_algebra_1.ipynb
lecture_20_ssl_autoencoders_gans.ipynb		lecture_20_ssl_autoencoders_gans.ipynb
lecture_21_natural_language_processing_1.ipynb		lecture_21_natural_language_processing_1.ipynb
lecture_22_natural_language_processing_2.ipynb		lecture_22_natural_language_processing_2.ipynb
lecture_2_linear_algebra_2.ipynb		lecture_2_linear_algebra_2.ipynb
lecture_3_calculus_1.ipynb		lecture_3_calculus_1.ipynb
lecture_4_calculus_2.ipynb		lecture_4_calculus_2.ipynb
lecture_5_probability.ipynb		lecture_5_probability.ipynb
lecture_6_statistics.ipynb		lecture_6_statistics.ipynb
lecture_7_machine_learning_intro.ipynb		lecture_7_machine_learning_intro.ipynb
lecture_8_machine_learning_regularization.ipynb		lecture_8_machine_learning_regularization.ipynb
lecture_9_svm.ipynb		lecture_9_svm.ipynb
path_utils.py		path_utils.py
presentation_global_vs_local_context.ipynb		presentation_global_vs_local_context.ipynb
self_supervised_learning_for_computer_vision.ipynb		self_supervised_learning_for_computer_vision.ipynb
self_supervised_representation_learning_general.ipynb		self_supervised_representation_learning_general.ipynb
summary_of_lectures(1-11).ipynb		summary_of_lectures(1-11).ipynb
workshop_1_car_price_prediction(complete).ipynb		workshop_1_car_price_prediction(complete).ipynb
workshop_1_car_price_prediction(exercise).ipynb		workshop_1_car_price_prediction(exercise).ipynb
workshop_1_heart_disease_prediction(complete).ipynb		workshop_1_heart_disease_prediction(complete).ipynb
workshop_1_heart_disease_prediction(exercise).ipynb		workshop_1_heart_disease_prediction(exercise).ipynb
workshop_2_sdss_classification(complete).ipynb		workshop_2_sdss_classification(complete).ipynb
workshop_2_sdss_classification(exercise).ipynb		workshop_2_sdss_classification(exercise).ipynb
workshop_3_dnn_implementation.ipynb		workshop_3_dnn_implementation.ipynb
workshop_3_dnn_implementation_complete.ipynb		workshop_3_dnn_implementation_complete.ipynb
workshop_4_optimizations.ipynb		workshop_4_optimizations.ipynb
workshop_5_cnn.ipynb		workshop_5_cnn.ipynb
workshop_6_detect_segment.ipynb		workshop_6_detect_segment.ipynb
workshop_7_aspect_based_sentiment_analysis.ipynb		workshop_7_aspect_based_sentiment_analysis.ipynb
workshop_7_question_answering_using_spacy_and_nmslib.ipynb		workshop_7_question_answering_using_spacy_and_nmslib.ipynb

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