Lab course materials for IEMBA 8/9 course "Coding and Artificial Intelligence"

Overview

IEMBA 8/9 - Coding and Artificial Intelligence

Course Banner

Dear IEMBA 8/9 students,

welcome to our IEMBA 8/9 elective course Coding and Artificial Intelligence, taught by Prof. Dr. Damian Borth and Prof. Dr. Barbara Weber. In this course, lectures and hands-on lab courses alternate to provide a better learning experience. Lab course materials for Python programming, Machine Learning und Deep Learning are available in and accessible through this repository.

Please use a laptop computer for the lab courses (not a tablet) to be able to fully participate in the exercises.

Happy Coding!

Your IEMBA teaching team


This table lists all coding lab notebooks and exercise notebooks:

Date Topic Lab Notebook Exercise Notebook Solution Notebook
< Mon, Jan 17 Prerequisite - Binder
Open In Colab
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Mon, Jan 17 Python 101: Jupyter Notebooks and Python Basics Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Tue, Jan 18, morning session Python 102: Numerical Math & Images Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Tue, Jan 18, afternoon session Machine Learning I
(Naive Bayes)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Tue, Jan 18, afternoon session Machine Learning II
(k Nearest-Neighbors)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Wed, Jan 19, morning session Deep Learning I
(Artificial Neural Nets)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Wed, Jan 19, afternoon session Deep Learning II
(Convolutional Neural Nets)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
< TBD Exam Exercise - Binder
Open In Colab
-
Owner
Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG
Deep Learning Research by AIML Team @ HSG
Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG
Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

Aquarius Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions NOTE: We are currently going through the open-source process requir

Zhiyuan YAO 0 Jun 02, 2022
Development kit for MIT Scene Parsing Benchmark

Development Kit for MIT Scene Parsing Benchmark [NEW!] Our PyTorch implementation is released in the following repository: https://github.com/hangzhao

MIT CSAIL Computer Vision 424 Dec 01, 2022
Codebase for testing whether hidden states of neural networks encode discrete structures.

structural-probes Codebase for testing whether hidden states of neural networks encode discrete structures. Based on the paper A Structural Probe for

John Hewitt 349 Dec 17, 2022
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Pytorch Squeeznet Pytorch implementation of Squeezenet model as described in https://arxiv.org/abs/1602.07360 on cifar-10 Data. The definition of Sque

gaurav pathak 86 Oct 28, 2022
Implementation of the final project of the course DDA6309 Probabilistic Graphical Model

Task-aware Joint CWS and POS (TCwsPos) This is the implementation of the final project of the course DDA6309 Probabilistic Graphical Models, The Chine

Peng 1 Dec 26, 2021
An OpenAI Gym environment for Super Mario Bros

gym-super-mario-bros An OpenAI Gym environment for Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The Nintendo Entertainment System (NES) us

Andrew Stelmach 1 Jan 05, 2022
Robust Self-augmentation for NER with Meta-reweighting

Robust Self-augmentation for NER with Meta-reweighting

Lam chi 17 Nov 22, 2022
Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

FAC-Net Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization Linjiang Huang (CUHK), Liang Wang (CASIA), Hongsheng

21 Nov 22, 2022
Tool for installing and updating MiSTer cores and other files

MiSTer Downloader This tool installs and updates all the cores and other extra files for your MiSTer. It also updates the menu core, the MiSTer firmwa

72 Dec 24, 2022
Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers

Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers The repository contains the code to reproduce the experimen

Alessandro Berti 4 Aug 24, 2022
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022
DI-smartcross - Decision Intelligence Platform for Traffic Crossing Signal Control

DI-smartcross DI-smartcross - Decision Intelligence Platform for Traffic Crossin

OpenDILab 213 Jan 02, 2023
AntroPy: entropy and complexity of (EEG) time-series in Python

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e

Raphael Vallat 153 Dec 27, 2022
Link prediction using Multiple Order Local Information (MOLI)

Understanding the network formation pattern for better link prediction Authors: [e

Wu Lab 0 Oct 18, 2021
FinRL­-Meta: A Universe for Data­-Driven Financial Reinforcement Learning. 🔥

FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users

AI4Finance Foundation 543 Jan 08, 2023
A community run, 5-day PyTorch Deep Learning Bootcamp

Deep Learning Winter School, November 2107. Tel Aviv Deep Learning Bootcamp : http://deep-ml.com. About Tel-Aviv Deep Learning Bootcamp is an intensiv

Shlomo Kashani. 1.3k Sep 04, 2021
CLOOB training (JAX) and inference (JAX and PyTorch)

cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train

Katherine Crowson 64 Nov 27, 2022
Article Reranking by Memory-enhanced Key Sentence Matching for Detecting Previously Fact-checked Claims.

MTM This is the official repository of the paper: Article Reranking by Memory-enhanced Key Sentence Matching for Detecting Previously Fact-checked Cla

ICTMCG 13 Sep 17, 2022
source code for 'Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge' by A. Shah, K. Shanmugam, K. Ahuja

Source code for "Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge" Reference: Abhin Shah, Karthikeyan Shanmugam, Kartik Ahu

Abhin Shah 1 Jun 03, 2022
YKKDetector For Python

YKKDetector OpenCVを利用した機械学習データをもとに、VRChatのスクリーンショットなどからYKKさん(もとい「幽狐族のお姉様」)を検出できるソフトウェアです。 マニュアル こちらから実行環境のセットアップから解説する詳細なマニュアルをご覧いただけます。 ライセンス 本ソフトウェア

あんふぃとらいと 5 Dec 07, 2021