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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

339 Enrolled
4.6
(55 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
路聽 聽 聽 聽Common biomedical features
路聽 聽 聽 聽Motivating examples
k-Nearest Neighbors (KNN)
路聽 聽 聽 聽Hyperparameter tuning (k)
路聽 聽 聽 聽Metrics (accuracy, F1 score, AUC, etc.)
Linear Classifiers
路聽 聽 聽 聽Logistic regression
路聽 聽 聽 聽The Softmax function (time permitting)
Support Vector Machines (SVM)
Decision Trees
路聽 聽 聽 聽Gini impurity and entropy
Random Forest
Gradient Boosting
路聽 聽 聽 聽Feature importance in tree-based algorithms
路聽 聽 聽 聽Handling imbalanced data
Model-Agnostic Feature Selection

Part 2

Neural Networks
路聽 聽 聽 聽Softmax
路聽 聽 聽 聽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)
路聽 聽 聽 聽One-hot encoding
路聽 聽 聽 聽Bag-of-words
路聽 聽 聽 聽Bigram models, N-gram models
路聽 聽 聽 聽Word embeddings (word2vec)
Advanced Topics
路聽 聽 聽 聽Transformers
路聽 聽 聽 聽Causal decoders and encoders
路聽 聽 聽 聽Fine-tuning (LoRA, PEFT)
路聽 聽 聽 聽Reinforcement fine-tuning
路聽 聽 聽 聽Retrieval-Augmented Generation (RAG) and memory
路聽 聽 聽 聽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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