Intro to Data Science Fall 2023
25 videos • 1,471 views • by Dr Mihai Nica
Recorded lectures from a course on introduction to data science
1
Intro to Data Science | Obsidian website with Wikipedia-style pages
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2
Intro to Data Science Lecture 1 | Learning Paradigms, Irreducible Error and Minimizing Square Loss
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3
Data's Goldilocks Problem | Overfitting vs Underfitting in Desmos
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4
Intro to Data Science Lecture 2 | Variance and Bias in Nearby Neighbour Averaging
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5
Intro to Data Science Lecture 3 | Vector programming in Python/NumPy and training vs test sets
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6
Intro to Data Science Lecture 4 | Train vs Test Error, Confidence Intervals and Meaning of Signicant
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7
DATA6100 - Some Project 1 Info (Recording from T Sept 26)
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8
Into To Data Science Lecture 5 | Multiple Linear Regression is Hard! Counterintuitive Coeffiecients
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9
Intro to Data Science Lecture 6 | Examples of Variable Selection and Some Practical Tips
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10
Intro to Data Science Lecture 7 | Classification and K-Nearest Neighbour examples on MNIST digits
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11
Intro to Data Science Lecture 8 | Cross Entropy Loss derivation
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12
Intro to Data Science Lecture 9 | Multi-class classification, Gradient Descent, and Titanic Dataset
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13
Intro to Data Science Lecture 10 | Bayes Theorem for Coins and Classifiers Kernel Density Estimation
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14
Intro to Data Science Lecture 11 | Quadratic discriminant analysis ROC curves and types of error
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15
Intro to Data Science Lecture 12 | Counting parameters and Naive Bayes on the Titanic Dataset
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16
Intro to Data Science Lecture 13 | Multiple hypothesis testing and Bootstraping
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17
Intro to Data Science Lecture 14 | Shrinkage methods and Ridge Regression / L2 Regularization
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18
Intro to Data Science Lecture 15 | Normalizing Variables in Ridge Regression and Goodharts Law
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19
Intro to Data Science Lecture 16 | Lasso Regressions / L1 Regularization and shapes of Lp norms
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20
Intro to Data Science Lecture 17 | The magic of eigenvector/values and Principle Component Analysis
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21
Intro to Data Science Lecture 18 | Examples of Principle Component Analysis and Vector Embeddings
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22
Intro to Data Science Lecture 19 | MNIST with JAX package, from linear regression to neural networks
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23
Intro to Data Science Lecture 20 | MNIST in JAX: softmax, cross entropy loss, Multilayer perceptron
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24
Intro to Data Science Lecture 21 | MNIST Neural net Regularization, autoencoders, word2vec overview
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25
Intro to Data Science Lecture 22 | letter2Vec (baby names version of word2vec)
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