Completed
Generative vs. Discriminative AI
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Neuro-Symbolic AI - Reviews and Tutorials
Automatically move to the next video in the Classroom when playback concludes
- 1 Generative vs. Discriminative AI
- 2 Thinking Fast and Thinking Slow: System 1 and System 2
- 3 Introduction to Logic Programming and Open World Reasoning
- 4 Introduction to Propositional Logic
- 5 Introduction to Language Models (LLM's, Prompt Engineering, Encoder/Decoder and more)
- 6 Probabilistic Graphical Models
- 7 STL: Signal Temporal Logic
- 8 Introduction to Gradient Descent
- 9 Technical Shortcoming of Deep Learning, pt. 1 of 2
- 10 Technical Shortcoming of Deep Learning, pt. 2 of 2
- 11 Introduction to Logic (for AI)
- 12 Inference in propositional logic
- 13 Tutorial: First Order Logic (FOL, Predicate Calculus)
- 14 Markov Random Fields, Markov Chains, Markov Logic Networks, and more
- 15 Introduction to Bayesian Reasoning
- 16 Training Challenges in Deep Learning
- 17 Backpropagation: Training Deep Neural Networks
- 18 The difference between AI and machine learning
- 19 Understanding the factors changing the distribution of data
- 20 Machine Learning and Discrimination
- 21 Basic Concepts in Bias and Fairness in Machine Learning
- 22 Discussion on select advances in neuro symbolic reasoning and learning
- 23 Regularization in Machine Learning
- 24 Transformer Architecture
- 25 Activation Functions used in Deep Neural Networks
- 26 Kernels and the Convolution Operation
- 27 Understanding RNNs
- 28 Embeddings
- 29 Deep Reinforcement Learning
- 30 Introduction to ML Ops
- 31 Distance Functions
- 32 Introduction to Probabilistic Soft Logic (PSL)
- 33 Applications of Reinforcement Learning
- 34 Parametric vs Non Parametric Approaches
- 35 k Nearest Neighbor
- 36 Unsupervised Learning and Clustering
- 37 k-Means Clustering
- 38 Hierarchical Clustering
- 39 Evaluating and Selecting Unsupervised Learning Methods
- 40 Introduction to Rule Learning and Itemset Mining
- 41 Itemset Mining and the A Priori Algorithm
- 42 Association Rule Mining
- 43 Semi-Supervised and Self-Supervised Learning
- 44 Ideas on Conducting Experiments in AI and ML
- 45 Quick and Dirty Intro to Neurosymbolic AI
- 46 Intro PyReason Tutorial: Pet Store Example
- 47 Advanced PyReason Tutorial
- 48 Pt I: PyReason - ML integration tutorial (binary classifier)
- 49 Pt II: PyReason - ML integration tutorial (time series reasoning)
- 50 Creating AI Ideas for Research
