CCAI Summer School | AI/ML and SWE/Python Self-Study Materials.xlsx

	A	B	C	D	E	F
1		Concept	Resources	Level	Est. time

2	AI introduction	General introduction of AI	UC Berkeley : Introduction to AI		2h30	Navigation :   Entry level —  Mid level —  Advanced level —
3			What is AI : Types, history and future
4			Top 18 Artificial Intelligence Applications in 2023
5			What is the difference between AI - ML - DL ?
6		Math for AI	All the Math You Need to Know in Artificial Intelligence		3h
7		Math for AI	Mathematics for Machine Learning and Data Science		3h
8		Intro ML	What is Machine Learning ?		1h
9		Intro ML	MIT : introduction to Machine Learning		1h
10		Intro DL	What is Deep Learning ?		10m
11		Intro DL	MIT : Deep Learning basics		10m
12		ML/DL problem framing	Problem Framing		3m
13	Tools for AI	Bash	Windows : Command Prompt Training		1h
14			Mac OS : Absolute Beginner Guide to the Mac OS Terminal
15			Linux : Beginner’s Guide to the Linux Terminal
16		Python / Anaconda & libraries	Main libraries : libraries & how to install them		1h30
17		Python / Anaconda & libraries	Python by Anaconda : Learn Python Basic for Data Analysis		1h30
18		IDE	Pycharm		1h30
19			Jupyter notebook
20			Microsoft VScode
21			Google Colab
22		Environments	Python/Conda environment		30m
23	Dataset management	Data processing	Numeric		2h
24			Categorical
25			Text
26			Image
27			Video
28		Dataset manipulation	Import Export data		1h30
29			Dataframe operations : 1 & 2
30			Sampling and splitting
31		Dataset cleaning	Data Cleaning with Python		40m
32		Dataset Analysis	General concepts		2h
33			Statistics
34			balanced - imbalanced
35			charts libraries : Matplotlib, Plotly, Seaborn
36	Ml / DL core	Supervised	Classification & regression		32h
37		Unsupervised	Dimentionality reduction, clustering, PCA		23h
38		Semi supervised	Classification case		30m
39		Reinforcement	Monte Carlo, Sarsa, Qlearning, …		5h
40		Active	Course & tutorial ModAL		4h
41		Generative models	GAN : WGANs, StyleGANs, …		4h
42		Transfer Learning	What is Transfer learning ?		30m
43	ML / DL applications	Computer vision	Intro		1h30
44			Classification
45			Semantic segmentation
46			Object detection
47			Instance segmentation (comparison with Semantic segmentation)
48			Tools for image/video annotation : LabelImg & Roboflow
49			Image processing and classification / object detection
50			Video classification
51		NLP	Intro		12h
52			Bag of words
53			Tokenization
54			Lemmatization
55			Stop Words
56			Stemming
57			Sentiment analysis, Semantic segmentation, Recommendation system, Chatbot, Machine translation
58			Text generation
59		Time series	Intro		2h
60			Features engineering
61			Time series analysis
62			Time series forecasting (XGBOOST, Prophet, NeuralProphet)
63	DL framework	Tools	Python libraries : Tensorflow, PyTorch (installation & uses), Keras		1h
64	DL framework	Tools	Google Colab GPU		1h
65	DL optimization	Optimization algorithms	Bases and code		3h
66	Model explanaibility		Intro		2h
67			Tutorial
68			Partial dependence plots
69			Permutations Importance
70	Model deployment		Intro and full course  Streamlite  Gradio  FastAPI, Docker, Heroku		12h
71	Bonus 🌿	ML/DL application for environment purpose	Machine Learning for Environmental Science and Engineering : Book, paper, code		10m
72	Bonus 🌿	ML/DL model carbon footprint	How to estimate and reduce the carbon footprint of machine learning models  Toward a Greener AI: Measuring the Carbon Footprint of a Deep Learning Model in Python		20m
73	Project collaboration	Tools	Github SourceTree		1h30
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