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Offered By: IBMSkillsNetwork

AI and ML in Biomedical Sciences: A Workshop for iBest 2024

This course contains items for the 2024 iBest workshop entitled "AI in Biomedical Sciences: A Comprehensive Workshop on Data Classification, Visualization, and Modelling". The first part of this course covers classification using a variety of methods including KNN, linear classifiers, SVM, and decision tree based methods including random forest and gradient boosting. The second part introduces neural networks, object detection, U-Net, and generative adversarial networks (GANs). The second part also includes large language models (LLMs), transformers, and AI agent development using LangChain.

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Course

Artificial Intelligence

157 Enrolled
4.5
(19 Reviews)

At a Glance

This course contains items for the 2024 iBest workshop entitled "AI in Biomedical Sciences: A Comprehensive Workshop on Data Classification, Visualization, and Modelling". The first part of this course covers classification using a variety of methods including KNN, linear classifiers, SVM, and decision tree based methods including random forest and gradient boosting. The second part introduces neural networks, object detection, U-Net, and generative adversarial networks (GANs). The second part also includes large language models (LLMs), transformers, and AI agent development using LangChain.


Course Syllabus

This is an in-person course; notes will only be available to attendees.

Part 1

Introduction to Biomedical Features
·       Common biomedical features
·       Motivating examples
k-Nearest Neighbors (KNN)
·       Hyperparameter tuning (k)
·       Metrics (accuracy, F1 score, AUC, etc.)
Linear Classifiers
·       Logistic regression
·       The Softmax function (time permitting)
Support Vector Machines (SVM)
Decision Trees
·       Gini impurity and entropy
Random Forest
Gradient Boosting
·       Feature importance in tree-based algorithms
·       Handling imbalanced data
Model-Agnostic Feature Selection

Part 2

Neural Networks
·       Softmax
·       Multilayer perceptron (MLP)
Convolutional Neural Networks (CNN)
Object Detection (featuring a take-home assignment)
U-Net
GenAI for Images using GAN (featuring a take-home assignment)
Large Language Models (LLMs)
·       One-hot encoding
·       Bag-of-words
·       Bigram models, N-gram models
·       Word embeddings (word2vec)
Advanced Topics
·       Transformers
·       Causal decoders and encoders
·       Fine-tuning (LoRA, PEFT)
·       Reinforcement fine-tuning
·       Retrieval-Augmented Generation (RAG) and memory
·       LangChain - Lab with LangChain, RAG, and Llama 3.1 405B/Mistral Large 2 123B

Estimated Effort

5 Hours

Level

Beginner

Industries

Healthcare

Skills You Will Learn

Artificial Intelligence, Computer Vision, LangChain, LLM, Machine Learning, PyTorch

Language

English

Course Code

AI0137EN

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