Merge branch 'master' of https://github.com/UndergraduateArtificialIn…

…telligenceClub/website

README.md

@@ -6,7 +6,7 @@
 [![Pull Requests Closed](https://img.shields.io/github/issues-pr-closed/UndergraduateArtificialIntelligenceClub/website)](https://github.com/UndergraduateArtificialIntelligenceClub/website/pulls)
 
 If you have any **feature requests**, please **open an issue** with the `enhancement` label.
-If you would like to **contribute**, please **contact** either [@NickNissen](https://github.com/NickNissen) or [@giancarlopernudisegura](https://github.com/giancarlopernudisegura) on [slack](https://albertaundergradai.slack.com) on the `#website` channel.
+If you would like to **contribute**, please **contact** either [@aarushb](https://github.com/aarushb), [@giancarlopernudisegura](https://github.com/giancarlopernudisegura) or [@Jacob-Winch](https://github.com/Jacob-Winch) on [Discord](https://discord.com/invite/KapmJxs) on the `#website` channel.
 
 ## Development Environment Setup
 The website is made with [Nuxt.js](https://nuxtjs.org/), a static site generator based on [Vue](https://vuejs.org/).

src/data/getting-started.md

@@ -1,19 +1,10 @@
-If you feel overwhelmed or have questions, ask on our [slack](/slack)!
+If you feel overwhelmed or have questions, ask on our [discord ](https://discord.com/invite/KapmJxs)!
 
-+ First, [Download python](https://www.python.org/downloads). Before doing so, you can check to see if you already have Python installed by opening a Terminal (Mac or Linux) or Command Prompt (Windows) and typing `python` or `python3`. If a shell opens, note the version number. For Tensorflow to work effectively, we recommend using **at least Python 3.6.7**.
-+ Once you have Python installed, pip should be installed by default. Try the following commands to install OpenAI Gym: `pip install gym`. If this doesn’t work, try the variants: `python -m pip install gym` or `py -m pip install gym` or `pip3 install gym`. **This will be dependent upon what command you type in the shell to make Python run.**
-+ Once you have Gym installed, begin working through the [Documentation tutorial](https://gym.openai.com/docs). To get the code running, we recommend either using [Wing101 Ide](https://wingware.com/downloads/wingide-101), [Visual Studio Code](https://code.visualstudio.com/), or [Sublime Text Editor](https://www.sublimetext.com/).
++ First, [Download python](https://www.python.org/downloads). Before doing so, you can check to see if you already have Python installed by opening a Terminal (Mac or Linux) or Command Prompt (Windows) and typing `python` or `python3`. If a shell opens, note the version number. We recommend using **at least Python 3.11**.
++ For an IDE we recommend either using [Wing101 Ide](https://wingware.com/downloads/wingide-101), [Visual Studio Code](https://code.visualstudio.com/), or [Sublime Text Editor](https://www.sublimetext.com/).
 + For two more non-standard IDE’s, consider using [Atom](https://atom.io/), [Jupyter Notebook](http://jupyter.org/), or [Vim](https://www.vim.org/). For the latter, type `pip install jupyter` [*see variants above*].
 + If you want some fun challenging exercises in Python to start playing around, consider
 	+ [GitHub Learning Lab](https://lab.github.com/)
 	+ [Code Academy](https://www.codecademy.com/learn/learn-python)
 	+ [Learn Python Tutorials](https://www.learnpython.org/)
 	+ [Challenging exercise](https://www.codewars.com/collections/basic-python)
-+ After this, we began working through this series of [OpenAI Gym tutorials](https://pythonprogramming.net/openai-cartpole-neural-network-example-machine-learning-tutorial/). We went through the first two steps of making the initial population.
-+ To apply neural networks, we have to then use Tensorflow. For the basics of Tensorflow:
-	+ [Tutorials Page](https://www.tensorflow.org/tutorials)
-	+ Adventures in Machine Learning:
-		+ [Tensorflow](http://adventuresinmachinelearning.com/python-tensorflow-tutorial/)
-		+ [Neural Networks](http://adventuresinmachinelearning.com/neural-networks-tutorial/)
-		+ [Deep Q Learning](http://adventuresinmachinelearning.com/reinforcement-learning-tensorflow/).
-	+ [Keras Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)

src/data/online_courses.md

@@ -0,0 +1,6 @@
++ [AI For Everyone](https://www.coursera.org/learn/ai-for-everyone)
+    + The "AI for Everyone" course on Coursera is a non-technical program that educates participants about AI, its potential applications, and its societal impacts, helping non-technical professionals understand and strategize around AI technologies.
++ [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction)
+    + The Machine Learning Specialization on Coursera, crafted by Andrew Ng, is a foundational program for beginners that teaches foundational AI concepts through a visual approach, followed by coding and mathematics. It's expanded into three courses and is designed to be approachable for those with no prior math or rigorous coding background, making core curriculum more accessible​.
++ [Hugging Face Natural Language Processing Course](https://huggingface.co/learn/nlp-course/chapter1/1)
+    + Hugging Face's NLP course is designed to provide a comprehensive understanding of natural language processing using the latest tools and techniques. It's geared towards practitioners looking to apply NLP in real-world scenarios, offering hands-on experience with cutting-edge models and libraries.

src/data/resources.json

@@ -2,90 +2,105 @@
 	"courses": [
 		{
 			"name": "Introduction to Data Science",
-			"number": "CMPUT 191",
-			"days": "TH",
-			"time": "14:00-15:20"
+			"number": "CMPUT 191"
+		},
+		{
+			"name": "Introduction to Principles and Techniques of Data Science",
+			"number": "CMPUT 195"
 		},
 		{
 			"name": "Ethics of Data Science and Artificial Intelligence",
-			"number": "CMPUT 200",
-			"days": "TH",
-			"time": "11:00-12:20"
+			"number": "CMPUT 200"
 		},
 		{
 			"name": "Game Artificial Intelligence",
-			"number": "CMPUT 256",
-			"days": "MWF",
-			"time": "11:00-11:50"
+			"number": "CMPUT 256"
 		},
 		{
 			"name": "Introduction to Artificial Intelligence",
-			"number": "CMPUT 261",
-			"days": "TH",
-			"time": "9:30-10:50"
+			"number": "CMPUT 261"
 		},
 		{
 			"name": "Basics of Machine Learning",
-			"number": "CMPUT 267",
-			"days": "TH",
-			"time": "12:30-13:50"
+			"number": "CMPUT 267"
 		},
 		{
 			"name": "Games, Puzzles, Algorithms",
-			"number": "CMPUT 355",
-			"days": "TH",
-			"time": "9:30-10:50"
+			"number": "CMPUT 355"
 		},
 		{
 			"name": "Introduction to Reinforcement Learning",
-			"number": "CMPUT 365",
-			"days": "MFW",
-			"time": "13:00-13:50"
+			"number": "CMPUT 365"
 		},
 		{
 			"name": "Search and Planning in Artificial Intelligence",
-			"number": "CMPUT 366",
-			"days": "TH",
-			"time": "14:00-15:20"
+			"number": "CMPUT 366"
 		},
 		{
 			"name": "Intermediate Machine Learning",
-			"number": "CMPUT 367",
-			"days": "TH",
-			"time": "15:30-16:50"
+			"number": "CMPUT 367"
 		},
 		{
 			"name": "Search, Knowledge and Simulation",
-			"number": "CMPUT 455",
-			"days": "TH",
-			"time": "14:00-15:20"
+			"number": "CMPUT 455"
+
 		},
 		{
 			"name": "Machine Learning",
-			"number": "CMPUT 463",
-			"days": "TH",
-			"time": "12:30-13:50"
+			"number": "CMPUT 466"
 		},
 		{
 			"name": "Artificial Intelligence Capstone",
-			"number": "CMPUT 469",
-			"days": "TH",
-			"time": "11:00-12:20"
+			"number": "CMPUT 469"
+		},
+		{
+			"name": "Probabilistic Graphical Models",
+			"number": "CMPUT 463"
+		},
+		{
+			"name": "Introduction to Natural Language Processing",
+			"number": "CMPUT 461"
+		},
+		{
+			"name": "Visual Recognition",
+			"number": "CMPUT 328"
+		},
+		{
+			"name": "Computer Vision",
+			"number": "CMPUT 428"
+		},
+		{
+			"name": "Ethics and Artificial Intelligence",
+			"number": "PHIL 385"
+		},
+		{
+			"name": "Statistical Methods for Learning and Data Mining",
+			"number": "STAT 441"
+		},
+		{
+			"name": "Visual Recognition",
+			"number": "CMPUT 328"
 		}
+
 	],
 	"lectures": [
 		{
 			"title": "General Machine Learning",
 			"content": [
 				{
 					"title": "Machine Learning with Andrew Ng, Stanford University",
-					"link": "https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN)",
+					"link": "https://www.youtube.com/watch?v=jGwO_UgTS7I&list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU",
 					"desc": "Universally recommended machine learning lectures by Andrew Ng, for beginner and intermediate level machine learning."
 				},
 				{
 					"title": "Elements of AI",
 					"link": "https://course.elementsofai.com/",
 					"desc": "Free online course made by the University of Helsinki and Reaktor."
+				},
+				{
+					"title": "Neural Networks: Zero to Hero with Andrej Karpathy",
+					"link": "https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ",
+					"desc": "A comprehensive educational resource that guides viewers from the basics of neural networks to more advanced concepts, showcasing practical examples and intuitive explanations. The series is known for its clear, engaging teaching style, making complex topics accessible to beginners and those looking to deepen their understanding of deep learning."
 				}
 			]
 		},
@@ -106,16 +121,29 @@
 					"title": "Deep Learning and Reinforcement Learning Summer School 2018",
 					"link": "http://videolectures.net/DLRLsummerschool2018_toronto/",
 					"desc": "These are the lecture videos from the 2018 DLRL summer school, held by CIFAR, Amii, and the Vector Institute in Toronto.  The DLRL summer school is pretty competitive, but the lectures actually vary from introductory to advanced.  Links to the slides are also included on the video pages."
-				},
-				{
-					"title": "Advanced Deep Learning and Reinforcement Learning with DeepMind",
-					"link": "https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs",
-					"desc": "A brand new lecture series (2018) done at UCL by a bunch of DeepMind staff."
 				}
 			]
 		}
 	],
 	"presentations": [
+		{
+			"title": "Data Cleaning Workshop",
+			"name": "Jacob Winch",
+			"link": "https://colab.research.google.com/drive/1nqyQoPrI4j5zNk1wZDOHWn9igIgSeIZj",
+			"desc": "A Google Colab notebook going over data cleaning in Python with Pandas, Matplotlib, and Seaborn."
+		},
+		{
+			"title": "scikit-learn Workshop",
+			"name": "Taran Purewal",
+			"link": "https://colab.research.google.com/drive/1t86MEw9lGtnNGfLHsroqkqS-q332PWK7?usp=sharing",
+			"desc": "A Google Colab notebook going over an intro to machine learning using scikit-learn."
+		},
+		{
+			"title": "UAIS Projects and AltaML Showcase",
+			"name": "UAIS Project Teams and Deanna Brousseau and Sevi Zhou from AltaML",
+			"link": "https://docs.google.com/presentation/d/1Nv89uCZENK3p6_vRMy-vLUPPWePKtGiH3NLGp5VTJCs/edit",
+			"desc": "A showcase of projects that the UAIS was working on in November 2023. Also an introduction to AltaML."
+		},
 		{
 			"title": "Welcome to UAIS",
 			"name": "Justin Stevens",

src/pages/getting-started.astro

@@ -2,6 +2,7 @@
 import Layout from '@layouts/Default.astro'
 import * as gettingStarted from '@data/getting-started.md'
 import * as datasets from '@data/datasets.md'
+import * as online_courses from '@data/online_courses.md'
 import { courses, lectures, etc as misc } from '@data/resources.json'
 ---
 
@@ -13,6 +14,12 @@ import { courses, lectures, etc as misc } from '@data/resources.json'
 		<div class="content" set:html={gettingStarted.compiledContent()}>
 		</div>
 	</section>
+	<section class="section">
+		<h1 class="title">Online Courses</h1>
+		<h4 class="subtitle">These are some introductory online courses to get you started</h4>
+		<div class="content" set:html={online_courses.compiledContent()}>
+		</div>
+	</section>
 	<section class="section">
 		<h1 class="title">Datasets</h1>
 		<h4 class="subtitle">These are some free datasets to use</h4>
@@ -26,15 +33,13 @@ import { courses, lectures, etc as misc } from '@data/resources.json'
 				<tr>
 					<th>Course Name</th>
 					<th>Course Number</th>
-					<th>Days and Time</th>
 				</tr>
 			</thead>
 			<tbody>
 				{courses.map(course =>
 					<tr>
 						<td>{course.name}</td>
 						<td>{course.number}</td>
-						<td>{course.days} {course.time}</td>
 					</tr>
 				)}
 			</tbody>

src/pages/index.astro

@@ -9,7 +9,7 @@ import Calendar from '@components/Calendar.astro'
 <>
 <astro>
   <section style="display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center;">
-    <img src="/images/logo.svg" alt="UAIS Logo" style="max-width: 48%; height: auto; max-height: 38%;">
+    <img src="/images/logo.svg" alt="UAIS Logo" style="max-width: 48%; height: auto; max-height: 50%;">
     <h1 class = "title" style="color: white;">Undergraduate Artificial Intelligence Society</h1>
   </section>	
 	<section class="section">
@@ -20,13 +20,7 @@ import Calendar from '@components/Calendar.astro'
 		We welcome all students regardless of field of study or AI knowledge.
 		You can check <a href="/constitution.pdf">our constitution</a>.
 	</section>
-	<section class="section">
-		<h2 class="title">Industry networking event!</h2>
-	<a href="https://www.eventbrite.ca/e/industry-networking-event-tickets-755172710907?aff=ebdsoporgprofile">
-		<img src="/images/Industry_night_final.png", alt="Poster for an industry networking event. Click this link to be redirected to the Eventbrite page."/>
-	</a>
-	</section>
-
+
 	<section class="section">
 		<h2 class="title">Current Projects</h2>
 		<h3 class="title">NHL Panic Index Project</h3>