lectures

• Lecture 1: Splines Smoothers and GAMs (part 1) (Jan. 25, 2021)
• Lecture 2: Splines Smoothers and GAMs (part 2) (Jan. 27, 2021)
• Lecture 3: Setup and Review of statsmodels (Jan. 29, 2021)
• Lecture 4: Splines Smoothers and GAMs (part 3) (Feb. 01, 2021)
• Lecture 5_5: Smoothers pyGAM csaps (Feb. 03, 2021)
• Lecture 5: Unsupervised learning cluster analysis (part 1) (Feb. 03, 2021)
• Lecture 6: Unsupervised learning cluster analysis (part 2) (Feb. 08, 2021)
• Lecture 7: Bayesian statistics (part 1) (Feb. 10, 2021)
• Lecture 8: Clustering in Python (Lab) (Feb. 12, 2021)
• Lecture 9: Bayesian statistics (part 2) (Feb. 17, 2021)
• Lecture 10: Bayes PyMC3 (Feb. 19, 2021)
• Lecture 11: Bayesian statistics (part 3) (Feb. 22, 2021)
• Lecture 12: Bayesian statistics (part 4) (Feb. 24, 2021)
• Lecture 13: Hierarchical Models (Lab) (Feb. 26, 2021)
• Lecture 14: Π CNNs basics (Mar. 03, 2021)
• Lecture 15: ⍺ CNNs Pooling and CNNs Structure (Mar. 05, 2021)
• Lecture 16: ύ Backprop max pooling, Receptive Fields and feature map viz (Mar. 08, 2021)
• Lecture 17: λ Saliency maps (Mar. 10, 2021)
• Lecture 18: 𝗈 State of the art models (SOTA) and Transfer Learning (Mar. 12, 2021)
• Lecture 19: ς RNNs (Mar. 15, 2021)
• Lecture 20: Π GRUs (Mar. 17, 2021)
• Lecture 21: ⍴ LSTMs (Mar. 19, 2021)
• Lecture 22: 💬 Language Modelling NLP 1/4 (Mar. 22, 2021)
• Lecture 23: 🔢 Language Representations NLP 2/4 (Mar. 24, 2021)
• Lecture 24: 🧠 Attention (Transformers I) NLP 3/4 (Mar. 26, 2021)
• Lecture 25: 🤖 (Transformers II) NLP 4/4 (Mar. 29, 2021)
• Lecture 26: ⍵ AutoEncoder (AE) (Apr. 02, 2021)
• Lecture 27: τ AE the Bayesian Approach (Apr. 05, 2021)
• Lecture 28: ο Variational AE 2 (Apr. 07, 2021)
• Lecture 29: π GANs (Apr. 09, 2021)
• Lecture 30: ⍺ GANs DOS (Apr. 12, 2021)
• Lecture 31: π Reinforcement Learning: Basics (Apr. 14, 2021)
• Lecture 32: ά Bellman equation, Optimality and Recursive algorithms (Apr. 16, 2021)
• Lecture 33: ς Deep Q-Learning (Apr. 19, 2021)