Thank you for tuning in to this audio only podcast presentation. This is week 121 of The Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for The Lindahl Letter is, “Considering an independent study applied AI syllabus.”
My initial take on writing an independent study based syllabus for applied AI was to find the best collection of freely available scholarly papers that somebody could read as an onramp to beginning to understand the field. That I think is a solid approach to helping somebody get going within a space that is very complex and full of content. It’s a space that is perpetually adding more content than any one person could possibly read or consume. Before you take that approach it is important to understand that one definitive textbook does exist. You certainly could go out and read it.
Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc..
You could find the first edition, second edition, or third edition for sale on eBay or somewhere else if you wanted a physical copy of the book. The book is currently in a 4th edition run, but I don’t have a copy of that edition yet. It’s used by over 1,500 schools so a lot of copies exist out in the wild . The authors Stewuart Russell and Peter Norvig have shared a PDF of the bibliography for that weighty tome of AI insights as well . Even with 35 pages of bibliography nobody with the name Lindahl made the cut. On a side note you can find the name Schmidhuber included twice if that sort of thing is important to you.
Let’s reset for a second here. If you are brand new to the field of AI or want to read a textbook based introduction, then you should seriously consider buying a copy of the aforementioned textbook. That is a really great way to start which has worked for tens of thousands of people. My approach here is going to be a little bit unorthodox, but it works for me. My last run at this type of effort was, “An independent study based introduction to machine learning syllabus for 2022” and you can find it out on Google Scholar . This outline will be the basis of a similar type of work that will end up getting crafted in Overleaf and shared out to the world.
Searching for just pure introductions to artificial intelligence is really hit or miss. A lot of different introductions to various fields exist. In this case, I’m trying to zoom out a little more into a larger evaluation of content instead of focusing on any one field. Nothing I ran into during my search had the number of citations or impact of the Russel and Norvig textbook. I’m going to endeavor to structure and organize 70+ articles into a syllabus. To give you an idea of the kind of things that are going to get pulled together here are 5 different papers.
Oke, S. A. (2008). A literature review on artificial intelligence. International journal of information and management sciences, 19(4), 535-570. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=f4dfcfe3f132b1079d054e2db29adc063fab0007
Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research, 20(5), e10775. https://www.sciencedirect.com/science/article/pii/S0004370218305988/pdfft?md5=ec6948d3f66efe5e57d1336a54d1604d&pid=1-s2.0-S0004370218305988-main.pdf
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6268174/
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459. https://www.repository.cam.ac.uk/bitstream/handle/1810/248538/Ghahramani%25202015%2520Nature.pdf?sequence=1
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4). https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf
Obviously, working out of a textbook format will help provide a better structured introduction and that is why I plan on mentioning that in the paper and I have noted it here. On a scholarly basis I understand the need to dive directly into source papers and really get a sense of what is happening within the applied side of AI research in the last 10 years. That is why I want to spend some time preparing the independent study based syllabus for others as well as it being a good solid effort for me to learn more rapidly with an extreme degree of focus.
What did ChatGPT 4.0 model come up with?
An independent study applied AI syllabus will provide you with the necessary knowledge and skills to develop practical AI solutions. Below is a suggested 14-week syllabus to guide you through the essential topics and resources.
- Week 1: Introduction to AI and Machine Learning
- History of AI
- Overview of machine learning, deep learning, and reinforcement learning
- Common machine learning algorithms
- Week 2: Python for AI and Machine Learning
- Basic Python programming
- NumPy, pandas, and matplotlib
- Setting up a Python environment for machine learning
- Week 3: Data Preprocessing and Exploration
- Data cleaning and transformation
- Handling missing data and outliers
- Feature engineering and selection
- Week 4: Supervised Learning – Regression
- Linear regression
- Ridge and Lasso regression
- Decision trees and random forests for regression
- Week 5: Supervised Learning – Classification
- Logistic regression
- k-Nearest Neighbors (k-NN)
- Support vector machines (SVM)
- Week 6: Unsupervised Learning
- k-Means clustering
- Hierarchical clustering
- Principal component analysis (PCA)
- Week 7: Neural Networks and Deep Learning
- Introduction to neural networks
- Activation functions and backpropagation
- Convolutional neural networks (CNNs)
- Week 8: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
- Introduction to RNNs and LSTMs
- Time series forecasting
- Natural language processing (NLP) applications
- Week 9: Reinforcement Learning
- Introduction to reinforcement learning
- Q-learning and Deep Q-Networks (DQNs)
- Policy gradients and actor-critic methods
- Week 10: Transfer Learning and Pretrained Models
- Introduction to transfer learning
- Fine-tuning pretrained models
- Pretrained models for NLP and computer vision
- Week 11: Natural Language Processing
- Word embeddings and Word2Vec
- Text classification and sentiment analysis
- Text summarization and translation
- Week 12: Computer Vision
- Object detection and recognition
- Semantic segmentation
- Image-to-image translation
- Week 13: Generative Models
- Introduction to generative models
- Variational autoencoders (VAEs)
- Generative adversarial networks (GANs)
- Week 14: Ethics and AI Deployment
- Ethical considerations in AI
- Bias and fairness in machine learning
- Deploying AI models
Key resources to study these topics include popular textbooks, online courses, blogs, and tutorials. Some notable resources include:
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto
- Online courses like Coursera’s “Machine Learning” by Andrew Ng or fast.ai’s “Practical Deep Learning for Coders”
- AI research papers from sources like arXiv.org, NeurIPS, and ICML
- By following this syllabus, you will gain a solid foundation in applied AI and be well-equipped to pursue more advanced topics or specific application domains.
What’s next for The Lindahl Letter?
- Week 122: Will AI be a platform or a service?
- Week 123: Considering open source AI
- Week 124: Profiling OpenAI
- Week 125: Profiling Hugging Face (open and collaborative machine learning)
- Week 126: Profiling Deep Mind
If you enjoyed this content, then please take a moment and share it with a friend. If you are new to The Lindahl Letter, then please consider subscribing. New editions arrive every Friday. Thank you and enjoy the year ahead.