How does a computer learn to sort good tomatoes from bad?
0:56 · Machine learning in 1 minute
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Could you write rules that spot every handwritten 4?
0:57 · Machine learning in 1 minute
Same fruits, no labels. Can a computer still group them?
0:55 · Machine learning in 1 minute
Is the sample number a useful feature for predicting a tomato's quality?
0:56 · Machine learning in 1 minute
Colour is red, green or blue. Code it as 1, 2, 3 for a linear model?
0:55 · Machine learning in 1 minute
Spam, cat or dog, house prices, tumors: which is a regression problem?
0:57 · Machine learning in 1 minute
A decision tree gets 100% on its training data. Does that tell you its test score?
0:57 · Machine learning in 1 minute
A test is 99% accurate. Is it a good test?
0:54 · Machine learning in 1 minute
A model says 'good' but the tomato is bad. What is that mistake called?
0:57 · Machine learning in 1 minute
Test A is 99% accurate, Test B is 95.8%. Which would you use?
0:56 · Machine learning in 1 minute
A test flags 48 people as possibly sick. How many are really sick?
0:57 · Machine learning in 1 minute
2% of 10,000 defective, precision 0.60, recall 0.90. How many get flagged?
0:57 · Machine learning in 1 minute
Model 1: precision 0.70, recall 0.85. Model 2: the reverse. Which job needs Model 1?
0:57 · Machine learning in 1 minute
A detector flags scores of 0.5 or more. Raise the bar to 0.7. What usually happens?
0:56 · Machine learning in 1 minute
A perfect model has an ROC AUC of 1. What does a coin-flipping model get?
0:57 · Machine learning in 1 minute
A tree fits its 400 training points exactly. Can it do worse than guessing the average?
0:57 · Machine learning in 1 minute
Precision 100%, recall 10%, average 55%. Is that a fair score?
0:57 · Machine learning in 1 minute
A program spots handwritten 4s with 88.56% accuracy. Is it any good?
0:55 · Machine learning in 1 minute
Why might MAE be preferred over MSE?
0:56 · Machine learning in 1 minute
A model's two errors are +5 and -5. Its mean error is 0. Is it perfect?
0:54 · Machine learning in 1 minute
What does entropy measure in a decision tree?
0:56 · Machine learning in 1 minute
Tennis data, 14 days. Why does 'Outlook' give the biggest information gain?
0:57 · Machine learning in 1 minute
A group is half yes, half no. What is its Gini impurity?
0:56 · Machine learning in 1 minute
Temperatures 40, 48, 60, 72, 80, 90. Where should a decision tree try to split?
0:56 · Machine learning in 1 minute
A regression tree leaf holds 30, 32 and 40 kWh. What does it predict?
0:56 · Machine learning in 1 minute
Does a deeper decision tree always do better on new data?
0:57 · Machine learning in 1 minute
A fully grown decision tree is cut back to fewer leaves. Can it do better on new data?
0:56 · Machine learning in 1 minute
A tree with 3 levels of questions fits a sine wave. How many values can it predict?
0:56 · Machine learning in 1 minute
Three curves fit the same 16 points. Which will predict new points best?
0:56 · Machine learning in 1 minute
Refit a line and a wiggly curve on 5 new samples. Whose predictions jump more?
0:56 · Machine learning in 1 minute
Add a column of dice rolls to a linear regression. Can its training error go up?
0:55 · Machine learning in 1 minute
California housing has 8 columns. Trying every set, how many models do you train?
0:56 · Machine learning in 1 minute
100 data points, 5-fold cross-validation. How many train the model each round?
0:55 · Machine learning in 1 minute
Rows sorted: 80 Yes, 20 No. Train on the first 80, test on the last 20. Will it work?
0:54 · Machine learning in 1 minute
You scale with the mean of all the data, then cross-validate. Is the test score honest?
0:57 · Machine learning in 1 minute
100 points, 10 positive, 5 random folds of 20. What is the chance a fold gets none?
0:57 · Machine learning in 1 minute
Draw 100 times from 100 items, putting each back. How many different items?
0:56 · Machine learning in 1 minute
3 models, each wrong 30% of the time, take a vote. How often is the vote wrong?
0:55 · Machine learning in 1 minute
A model gets 2 of 10 points wrong. In boosting, which does the next one focus on?
0:56 · Machine learning in 1 minute
In AdaBoost, a small model is wrong 90% of the time. How much say should it get?
0:56 · Machine learning in 1 minute
A random forest split sees 2 of 4 measurements. How often is the best one hidden?
0:56 · Machine learning in 1 minute
Line 1 passes through 3 of 4 points, line 2 through none. Which has less squared error?
0:57 · Machine learning in 1 minute
Four points lie on a rising line. Move one. Can the best-fit slope turn negative?
0:52 · Machine learning in 1 minute
Linear regression fits straight lines. Can it fit a curve?
0:57 · Machine learning in 1 minute
Make a curve fit its training points worse on purpose. Can it do better on new points?
0:55 · Machine learning in 1 minute
Ridge and lasso both shrink weights. Which one can make a weight exactly 0?
0:55 · Machine learning in 1 minute
Sales 3, 5, 4, 6, 5, 7, 6, 8. A model uses the last 2 days. What does it predict next?
0:57 · Machine learning in 1 minute
Four data sets share one best-fit line and the same average miss. Do they look alike?
0:57 · Machine learning in 1 minute
Ridge with penalty 100 is fit to (1, 101) and (2, 102). What does it predict at x = 1?
0:57 · Machine learning in 1 minute
u = (1, 2, 3), v = (1, 0, 2). What is the rank of the 3 by 3 matrix u vᵀ?
0:57 · Machine learning in 1 minute
Priya never rated Sholay. Can the other ratings predict hers?
0:56 · Machine learning in 1 minute
f(x) = x², start at x = 4, step size 0.1. Where is x after one gradient descent step?
0:56 · Machine learning in 1 minute
f(x) = x², start at 4, step size 1.1. Does gradient descent reach the bottom?
0:57 · Machine learning in 1 minute
Each step nudges a line toward one random point. Does the error drop every step?
0:56 · Machine learning in 1 minute
Gradient descent stops where the slope is 0. Is that the lowest point?
0:56 · Machine learning in 1 minute
Gradient descent needs 27 steps down x² from x = 4. How many does Newton's method need?
0:56 · Machine learning in 1 minute
A model's score is 0. What probability does the sigmoid turn it into?
0:54 · Machine learning in 1 minute
Logistic regression with two inputs: what shape is the border between yes and no?
0:56 · Machine learning in 1 minute
The true class gets probability 0.01. Which loss punishes it more?
0:54 · Machine learning in 1 minute
Softmax turns the scores 2, 1, 0 into probabilities. What does the 0 class get?
0:55 · Machine learning in 1 minute
A straight line sorts tomatoes from oranges. Add one huge orange. Does the cut move?
0:56 · Machine learning in 1 minute
Weights 1 and 1, bias -1.5: which logic gate is this neuron?
0:56 · Machine learning in 1 minute
Can one straight line put the blue points on one side and the amber on the other?
0:57 · Machine learning in 1 minute
What is the fewest number of straight lines that separates XOR?
0:54 · Machine learning in 1 minute
Two layers with no activation, y = 3 · (2x). More powerful than one layer?
0:54 · Machine learning in 1 minute
f = (x + y) × z at x = 2, y = 3, z = 4. Nudge x by 0.01: how much does f go up?
0:56 · Machine learning in 1 minute
784 inputs, 100 hidden units, 1 output. How many numbers does the network learn?
0:57 · Machine learning in 1 minute
A 5 by 5 filter slides over a 28 by 28 image. How big is the output?
0:56 · Machine learning in 1 minute
Two 70 x 70 boxes, one shifted 20 right and 20 down. How much do they overlap?
0:57 · Machine learning in 1 minute
What happens to gradients in a simple RNN during backpropagation?
0:54 · Machine learning in 1 minute
A language model reads "the cat sat on the". What does it output?
0:55 · Machine learning in 1 minute
LeNet-5 has 61,706 weights. How many passes does backpropagation need for every slope?
0:57 · Machine learning in 1 minute
The slope of 3x² + 2x + 1 at x = 2 is 14. Nudge x by 10⁻²⁰: what slope do you get?
0:57 · Machine learning in 1 minute
LeNet-5 learns 61,706 numbers in 5 layers. Where are most of them?
0:57 · Machine learning in 1 minute
Freeze a trained image model, train only a 7,690-number last layer. Test accuracy?
0:57 · Machine learning in 1 minute
New fruit: orange or apple? Its 3 nearest neighbours vote.
0:56 · Machine learning in 1 minute
k-NN uses height (metres) and income (rupees). Which one decides who is nearest?
0:56 · Machine learning in 1 minute
A million stored points: must k-NN compare a new point with every one?
0:57 · Machine learning in 1 minute
A ball just fits inside a cube. In 10 dimensions, how much of the cube does it fill?
0:53 · Machine learning in 1 minute
Tennis on a sunny, cool, humid, windy day: what does naive Bayes predict?
0:56 · Machine learning in 1 minute
Point A is at (3, 0), point B at (2, 2). Which is nearer to (0, 0)?
0:57 · Machine learning in 1 minute
All 4 overcast days were "yes". What chance does naive Bayes give "no" to the next one?
0:57 · Machine learning in 1 minute
Both lines make 0 mistakes. Which is safer for new points?
0:54 · Machine learning in 1 minute
Blue points sit inside a ring of amber. Can a straight line split them?
0:56 · Machine learning in 1 minute
One amber point sits among the blue ones. Must the dividing line twist to fit it?
0:56 · Machine learning in 1 minute
An SVM's border sits at 0. Remove the blue point at 2. Where is the border now?
0:57 · Machine learning in 1 minute
Points 1, 2, 3, 7, 8, 9, centres at 0 and 5: where are they after one k-means step?
0:56 · Machine learning in 1 minute
More centres always lower k-means' error. So is the k with the lowest error the best?
0:57 · Machine learning in 1 minute
Six points in one clump, three in another, one alone. Must the lone point join a group?
0:57 · Machine learning in 1 minute
No labels, only a reward at the end. What does the agent learn from?
0:57 · Machine learning in 1 minute
A car too weak to climb the hill pushes right all the time. How many steps to the flag?
0:56 · Machine learning in 1 minute
Where do loss functions come from? | Deep Learning, Lecture 1
2:54 · Deep learning in 3 minutes
Where MSE and cross-entropy come from | Deep Learning, Lecture 2
2:53 · Deep learning in 3 minutes
Outliers: how the noise model picks the loss | Deep Learning, Lecture 3A
2:55 · Deep learning in 3 minutes
Priors: where regularization comes from | Deep Learning, Lecture 3B
2:54 · Deep learning in 3 minutes
From weighted sums to neurons: solving XOR | Deep Learning, Lecture 4A
2:59 · Deep learning in 3 minutes
ReLU hinges and universal approximation | Deep Learning, Lecture 4B
2:53 · Deep learning in 3 minutes
SGD vs minibatch: how much data per step? | Deep Learning, Lecture 5A
3:00 · Deep learning in 3 minutes
Why gradient descent zigzags: step sizes | Deep Learning, Lecture 5B
2:52 · Deep learning in 3 minutes
Backprop by hand: one local rule | Deep Learning, Lecture 6A
3:03 · Deep learning in 3 minutes
Build a tiny autograd engine from scratch | Deep Learning, Lecture 6B
2:59 · Deep learning in 3 minutes
Momentum: averaging away the zigzag | Deep Learning, Lecture 7A
3:03 · Deep learning in 3 minutes
RMSProp and Adam: a step size per coordinate | Deep Learning, Lecture 7B
3:14 · Deep learning in 3 minutes
Backprop through layers: Jacobians to batches | Deep Learning, Lecture 8
3:04 · Deep learning in 3 minutes
Valid outputs by design: softplus to softmax | Deep Learning, Lecture 9A
2:59 · Deep learning in 3 minutes
Learning-rate schedules: warmup, decay, cosine | Deep Learning, Lecture 9B
2:53 · Deep learning in 3 minutes
Overfitting, weight decay and early stopping | Deep Learning, Lecture 10A
3:08 · Deep learning in 3 minutes
Dropout, augmentation, Mixup and label smoothing | Deep Learning, Lecture 10B
3:08 · Deep learning in 3 minutes
Next-token prediction from scratch | Attention series, Part 1
3:01 · Deep learning in 3 minutes
Why attention? | Attention series, Part 2A
2:32 · Deep learning in 3 minutes
Query, key, value | Attention series, Part 2B
3:05 · Deep learning in 3 minutes
From attention to prediction | Attention series, Part 2C
3:12 · Deep learning in 3 minutes
Position and order | Attention series, Part 2D
2:56 · Deep learning in 3 minutes
What Is Machine Learning? | Module 1 Ep 1
1:20 · Machine learning in 3 minutes
Types of Machine Learning | Module 1 Ep 2
1:20 · Machine learning in 3 minutes
Training vs Testing Data | Module 1 Ep 3
1:20 · Machine learning in 3 minutes
Classification vs Regression | Module 1 Ep 4
1:07 · Machine learning in 3 minutes
The Accuracy Trap in ML | Module 1 Ep 5
1:28 · Machine learning in 3 minutes
Confusion Matrix Explained | Module 1 Ep 6
1:36 · Machine learning in 3 minutes
Precision vs Recall | Module 1 Ep 7
1:29 · Machine learning in 3 minutes
F1 Score and Trade-offs | Module 1 Ep 8
1:17 · Machine learning in 3 minutes
ROC Curve and AUC | Module 1 Ep 9
1:33 · Machine learning in 3 minutes
What Is a Decision Tree? | Module 2 Ep 1
3:01 · Machine learning in 3 minutes
Entropy Explained | Module 2 Ep 2
3:35 · Machine learning in 3 minutes
Information Gain Explained | Module 2 Ep 3
4:14 · Machine learning in 3 minutes
Building a Decision Tree? | Module 2 Ep 4
3:41 · Machine learning in 3 minutes
Gini Impurity Explained | Module 2 Ep 5
2:18 · Machine learning in 3 minutes
Regression Trees? | Module 2 Ep 6
3:28 · Machine learning in 3 minutes
Handling Continuous Features? | Module 2 Ep 7
4:50 · Machine learning in 3 minutes
Decision Boundaries? | Module 2 Ep 8
3:25 · Machine learning in 3 minutes
Overfitting and Pruning? | Module 2 Ep 9
3:44 · Machine learning in 3 minutes
Decision Trees in Code? | Module 2 Ep 10
2:40 · Machine learning in 3 minutes
Overfitting and Underfitting? | Module 3 Ep 1
2:48 · Machine learning in 3 minutes
The Bias Variance Tradeoff? | Module 3 Ep 2
3:10 · Machine learning in 3 minutes
Cross-Validation? | Module 3 Ep 3
2:39 · Machine learning in 3 minutes
Cross-Validation Variants? | Module 3 Ep 4
3:40 · Machine learning in 3 minutes
Introduction to Ensembles? | Module 3 Ep 5
3:10 · Machine learning in 3 minutes
Bagging - Bootstrap Aggregating? | Module 3 Ep 6
3:47 · Machine learning in 3 minutes
Random Forests? | Module 3 Ep 7
4:00 · Machine learning in 3 minutes
Introduction to Boosting? | Module 3 Ep 8
3:48 · Machine learning in 3 minutes
AdaBoost? | Module 3 Ep 9
3:38 · Machine learning in 3 minutes
Feature Importance? | Module 3 Ep 10
4:02 · Machine learning in 3 minutes
Vectors & Dot Products | Module 4 Ep 1
1:37 · Machine learning in 3 minutes
Spans, Independence & Rank | Module 4 Ep 2
1:50 · Machine learning in 3 minutes
Surface Plots | Module 4 Ep 3
2:04 · Machine learning in 3 minutes
Contour Plots | Module 4 Ep 4
1:40 · Machine learning in 3 minutes
Gradients | Module 4 Ep 5
1:42 · Machine learning in 3 minutes
Matrix Calculus Identities | Module 4 Ep 6
1:50 · Machine learning in 3 minutes
What is Linear Regression? | Module 5 Ep 1
2:34 · Machine learning in 3 minutes
Matrix Formulation | Module 5 Ep 2
2:37 · Machine learning in 3 minutes
The Error & Objective | Module 5 Ep 3
3:17 · Machine learning in 3 minutes
Deriving the Normal Equation | Module 5 Ep 4
3:06 · Machine learning in 3 minutes
Worked Example | Module 5 Ep 5
2:49 · Machine learning in 3 minutes
Effect of Outliers | Module 5 Ep 6
2:26 · Machine learning in 3 minutes
Basis Expansion | Module 5 Ep 7
2:37 · Machine learning in 3 minutes
Geometric Interpretation | Module 5 Ep 8
3:05 · Machine learning in 3 minutes
Multicollinearity | Module 5 Ep 9
2:38 · Machine learning in 3 minutes
Dummy Variables and Assumptions | Module 5 Ep 10
2:51 · Machine learning in 3 minutes
The Overfitting Problem | Module 6 Ep 1
2:06 · Machine learning in 3 minutes
Ridge Regression: The Idea | Module 6 Ep 2
2:10 · Machine learning in 3 minutes
Geometric View of Ridge | Module 6 Ep 3
2:06 · Machine learning in 3 minutes
Ridge Solution and Properties | Module 6 Ep 4
2:24 · Machine learning in 3 minutes
Bias-Variance Tradeoff | Module 6 Ep 5
1:49 · Machine learning in 3 minutes
Cross-Validation for Lambda | Module 6 Ep 6
1:57 · Machine learning in 3 minutes
Lasso: The L1 Penalty | Module 6 Ep 7
2:08 · Machine learning in 3 minutes
Why Lasso Gives Sparsity | Module 6 Ep 8
2:20 · Machine learning in 3 minutes
Solving Lasso: Coordinate Descent | Module 6 Ep 9
2:22 · Machine learning in 3 minutes
Ridge vs Lasso and Elastic Net | Module 6 Ep 10
2:21 · Machine learning in 3 minutes
Why Linear Regression Fails for Classification | Module 7 Ep 1
3:02 · Machine learning in 3 minutes
The Sigmoid Function | Module 7 Ep 2
3:33 · Machine learning in 3 minutes
The Logistic Regression Model | Module 7 Ep 3
3:39 · Machine learning in 3 minutes
Decision Boundary | Module 7 Ep 4
3:48 · Machine learning in 3 minutes
Why Not Squared Error? | Module 7 Ep 5
3:16 · Machine learning in 3 minutes
Cross-Entropy Loss via MLE | Module 7 Ep 6
3:50 · Machine learning in 3 minutes
Gradient and Learning | Module 7 Ep 7
2:03 · Machine learning in 3 minutes
Non-Linear Decision Boundaries | Module 7 Ep 8
3:49 · Machine learning in 3 minutes
Multi-Class with Softmax | Module 7 Ep 9
5:03 · Machine learning in 3 minutes
From Features to Learning | Module 8 Ep 1
1:22 · Machine learning in 3 minutes
The Perceptron | Module 8 Ep 2
1:33 · Machine learning in 3 minutes
Logic Gates with Perceptrons | Module 8 Ep 3
1:38 · Machine learning in 3 minutes
The XOR Problem | Module 8 Ep 4
1:48 · Machine learning in 3 minutes
Activation Functions | Module 8 Ep 5
2:01 · Machine learning in 3 minutes
The Multi-Layer Perceptron | Module 8 Ep 6
1:42 · Machine learning in 3 minutes
Forward Propagation | Module 8 Ep 7
1:48 · Machine learning in 3 minutes
Vectorized Forward Propagation | Module 8 Ep 8
1:51 · Machine learning in 3 minutes
XOR Solved by an MLP | Module 8 Ep 9
1:49 · Machine learning in 3 minutes
Parameter Counting | Module 8 Ep 10
1:44 · Machine learning in 3 minutes
Why Automatic Differentiation? | Module 9 Ep 1
2:40 · Machine learning in 3 minutes
Computational Graphs | Module 9 Ep 2
2:29 · Machine learning in 3 minutes
Backpropagation Through a Graph | Module 9 Ep 3
3:01 · Machine learning in 3 minutes
Full Backward Pass Example | Module 9 Ep 4
3:08 · Machine learning in 3 minutes
Building an Autograd Engine | Module 9 Ep 5
2:46 · Machine learning in 3 minutes
What is Next Token Prediction? | Module 10 Ep 1
1:47 · Machine learning in 3 minutes
The Indian Names Dataset | Module 10 Ep 2
1:31 · Machine learning in 3 minutes
Building the Training Set | Module 10 Ep 3
1:56 · Machine learning in 3 minutes
Character Embeddings | Module 10 Ep 4
1:56 · Machine learning in 3 minutes
The Embedding Lookup | Module 10 Ep 5
1:50 · Machine learning in 3 minutes
The Full Architecture | Module 10 Ep 6
1:54 · Machine learning in 3 minutes
Training with Cross-Entropy | Module 10 Ep 7
2:06 · Machine learning in 3 minutes
Generating New Names | Module 10 Ep 8
2:02 · Machine learning in 3 minutes
Flip two fair coins. Which is more likely: two heads, or one of each?
0:54 · Probability & statistics in 1 minute
How many events does one die roll have?
0:55 · Probability & statistics in 1 minute
Roll a die 4 times. What is the chance of at least one six?
0:55 · Probability & statistics in 1 minute
10 people, and every pair shakes hands once. How many handshakes?
0:55 · Probability & statistics in 1 minute
23 people in a room. What is the chance two share a birthday?
0:56 · Probability & statistics in 1 minute
You pick door 1 of 3. The host knows where the car is and opens door 3: a goat. Switch?
0:57 · Probability & statistics in 1 minute
5% are sick. A test catches 90% of them, flags 5% of the rest. You test positive. Sick?
0:56 · Probability & statistics in 1 minute
5% are sick. A test catches 90% of them and flags 5% of the rest. Positive twice. Sick?
0:56 · Probability & statistics in 1 minute
A fair coin lands heads 5 times in a row. Chance the next flip is heads?
0:55 · Probability & statistics in 1 minute
Teacher A has the higher pass rate in each section. Can teacher B be higher overall?
0:55 · Probability & statistics in 1 minute
Roll two dice and add them. Which total comes up most often?
0:55 · Probability & statistics in 1 minute
Roll two dice and keep the bigger one. Which value is most likely?
0:55 · Probability & statistics in 1 minute
A box has 10 eggs, each cracked with chance 10%. Most likely number of cracked eggs?
0:56 · Probability & statistics in 1 minute
A help desk averages 6 calls an hour. Which is more likely: 5 calls or 6?
0:57 · Probability & statistics in 1 minute
Roll a die until you get a six. Which number of rolls is most likely?
0:56 · Probability & statistics in 1 minute
Prices end in random paise. Round to the nearest rupee. Average size of the error?
0:57 · Probability & statistics in 1 minute
Waits at a counter average 10 minutes (exponential). What share wait under 10?
0:57 · Probability & statistics in 1 minute
Marks follow a bell curve: average 70, spread (SD) 8. What share score above 86?
0:57 · Probability & statistics in 1 minute
Can a probability density be bigger than 1?
0:57 · Probability & statistics in 1 minute
A loaded die: faces 1 to 4 take slices of 0 to 1 in order. Which face gets 0.45?
0:57 · Probability & statistics in 1 minute
10 people get their hats back, shuffled. On average, how many get their own?
0:57 · Probability & statistics in 1 minute
100 fair coin flips: win or lose Rs 100 each. How far from 0 do you typically end?
0:57 · Probability & statistics in 1 minute
You know only a data set's mean and SD (spread). What share must lie within 2 SDs?
0:57 · Probability & statistics in 1 minute
A dart lands at random in a 2 x 2 square. Chance it hits the circle that fits inside?
0:57 · Probability & statistics in 1 minute
X is -2, -1, 0, 1 or 2, equally likely, and Y = X squared. What is their correlation?
0:57 · Probability & statistics in 1 minute
Ice-cream sales and sunburn cases correlate (0.99). Does ice cream cause sunburn?
0:57 · Probability & statistics in 1 minute
Scale errors (independent): SD 3 g and SD 4 g. Add their readings: SD of the total?
0:57 · Probability & statistics in 1 minute
20 above average on test 1. Tests correlate 0.5, same spread. Expected test 2 score?
0:57 · Probability & statistics in 1 minute
7 heads in 10 fair flips. After 1,000 more flips, what share of heads do you expect?
0:56 · Probability & statistics in 1 minute
A fair coin. Which is more likely to give 60% or more heads: 10 flips or 100 flips?
0:57 · Probability & statistics in 1 minute
One die: each face equally likely. Add up 10 dice. What shape do the totals make?
0:57 · Probability & statistics in 1 minute
Run a random-number generator twice from the same seed. Same numbers?
0:57 · Probability & statistics in 1 minute
A poll of 100 people is good to about ±10 points. How many for ±5 points?
0:57 · Probability & statistics in 1 minute
A coin lands 7 heads in 10 flips. Which chance of heads makes that most likely?
0:57 · Probability & statistics in 1 minute
Python multiplies 1,100 chances of 0.5 together. What does it get?
0:57 · Probability & statistics in 1 minute
The new bar looks 3 times taller than the old one. How much did sales grow?
0:57 · Probability & statistics in 1 minute
The line climbs the same height each year. Did it grow the same each year?
0:57 · Probability & statistics in 1 minute
Class A: 66, 68, 70, 72, 74. Class B: 40, 45, 70, 95, 100. Which average is higher?
0:57 · Probability & statistics in 1 minute
Nine earn Rs 30,000 each, one earns Rs 300,000. What is the average pay?
0:57 · Probability & statistics in 1 minute
Roll one die. What is the chance of an even number or a number above 3?
0:57 · Probability & statistics in 1 minute
One die: "it shows 1" and "it shows 2" can never happen together. Are they independent?
0:57 · Probability & statistics in 1 minute
50% of passwords are digits, 30% letters, 20% symbols. What share get hacked?
0:56 · Probability & statistics in 1 minute
5% are sick. Which test upgrade makes a positive result more trustworthy?
0:57 · Probability & statistics in 1 minute
Pick a number from 0 to 1 at random and square it. Chance the square is below 0.25?
0:57 · Probability & statistics in 1 minute
10,000 bits, each wrong with chance 1 in 10,000. Chance the block has no errors?
0:57 · Probability & statistics in 1 minute
Normal or Laplace, same centre and same SD of 1: which gives more values beyond 3?
0:57 · Probability & statistics in 1 minute
Heads (30%) wins Rs 10, tails loses Rs 5. After 100 plays, who comes out ahead?
0:57 · Probability & statistics in 1 minute
Roll a die and square the number. What is the average of the squares?
0:57 · Probability & statistics in 1 minute
A coin lands heads 90% of the time. At least how many bits to store 1,000 flips?
0:58 · Probability & statistics in 1 minute
X and Y are independent, each between 10 and 11. Cosine similarity 0.999. Are they linked?
0:57 · Probability & statistics in 1 minute
Points spread 5 across and 2 up, tilted. How much spread can one line keep?
0:58 · Probability & statistics in 1 minute
Next-letter weights add to 0.8. Scaled to add to 1, how often is "e" picked?
0:57 · Probability & statistics in 1 minute
A box plot of 20 marks. How many of the 20 marks lie inside the box?
0:57 · Probability & statistics in 1 minute
x = 1 + 2**3 / 4 * 5. What does print(x) show?
0:57 · Python in 1 minute
print(6 // 2, 6 / 2, 6 % 2): three kinds of division. What prints?
0:57 · Python in 1 minute
print(365 ^ 365) was meant to give 365 to the power 365. Why does it print 0?
0:57 · Python in 1 minute
a = 10, b = 5, a = a + b, b = a + b. What does print(a, b) show?
0:57 · Python in 1 minute
print("Hello", "World") prints Hello World. Where does the space come from?
0:57 · Python in 1 minute
x = x + y, y = x - y, x = x - y. Does this swap x and y?
0:57 · Python in 1 minute
x = input(), y = input(). You type 10, then 20. What does print(x + y) show?
0:57 · Python in 1 minute
A coach number should print as S10. Does print("S" + 10) work?
0:56 · Python in 1 minute
x = 12373626527645726457264527634. How many digits? What does len(str(x)) print?
0:57 · Python in 1 minute
x = 9. If x is more than 10 print Yes, else print No, then Done! What prints?
0:57 · Python in 1 minute
x = 10, then if x = 10: print("ten"). Spot the bug. What happens?
0:57 · Python in 1 minute
marks = 0, then if marks: print Yes., else print No. No comparison. What prints?
0:57 · Python in 1 minute
What is 13 in binary? Divide by 2 and keep the remainders
0:57 · Python in 1 minute
Can 4 binary digits after the point hold 0.2? What do these prints show?
0:57 · Python in 1 minute
if 0==1 and 1/0==1: does Python print Yes, No, or crash with an error?
0:58 · Python in 1 minute
x is 0. Does if x != 0 and 1/x is more than 1/100 crash?
0:57 · Python in 1 minute
marks is 95 and the grade should be A. What does this if, if, else code print?
0:57 · Python in 1 minute
print(False and True == False and True): does it print True or False?
0:53 · Python in 1 minute
Rock 0, paper 1, scissors 2. You play rock, the computer plays paper. What prints?
0:57 · Python in 1 minute
total starts at 0 and a while loop adds i while i is at most 4. What prints?
0:58 · Python in 1 minute
n = 5, num = 1. What does this if print, and what changes with while?
0:57 · Python in 1 minute
x = 1, while x is below 5: print x, add 1. Then print x again. What is the last number?
0:55 · Python in 1 minute
This loop should print 7, the square root of 49. Why does it print 6?
0:57 · Python in 1 minute
start = 1.0, halve it while it is above 0.0. Does "Fin." ever print?
0:57 · Python in 1 minute
Is 91 prime? This loop looks for its smallest divisor. What does it print?
0:56 · Python in 1 minute
Is 197 prime? Do you really have to try 195 divisors to find out?
0:57 · Python in 1 minute
i = 0, s = 0. The loop adds i to s but skips even i with continue. What prints?
0:57 · Python in 1 minute
x = 1. while x is below 5: break, then x = x + 1. What does print(x) show?
0:52 · Python in 1 minute
A 3 x 3 times table with a loop inside a loop. Why does only one row print?
0:51 · Python in 1 minute
k = 0 and i = 5. A while loop inside a while loop adds j to k. What is k at the end?
0:52 · Python in 1 minute
scores = [90, 82, 83, 100, 102]. What do len, [0], [-1] and [5] give?
0:57 · Python in 1 minute
x = [] then x.append(10) prints [10]. Why does y = y.append(10) make y None?
0:57 · Python in 1 minute
x.append([5, 6, 7]) or x.extend([5, 6, 7]): what does each list print?
0:57 · Python in 1 minute
x = [1] * 2 and y = x * 2. What does print(x, y) show in Python?
0:55 · Python in 1 minute
x = ["a", "b", "c", "d", "e"]. Which letters does x[1:4] give in Python?
0:57 · Python in 1 minute
x = ["a", "b", "c", "d", "e"]. What do x[::-1], x[4:0:-1] and x[4:-1:-1] give?
0:57 · Python in 1 minute
x has 5 items. Which crashes in Python: x[0:10], x[4:2] or x[10]?
0:57 · Python in 1 minute
f = f * i for i in range(11) should give 10 factorial. Why does it print 0?
0:56 · Python in 1 minute
x = 10.0, then for x in range(2): print x, add 1, print x. What prints?
0:57 · Python in 1 minute
All prefixes of [1, 2, 3] with range(len(xs)). What does print(ys) show?
0:57 · Python in 1 minute
highest = 0, then keep the biggest of [-5, -2, -9]. What does it print?
0:57 · Python in 1 minute
xs[i] = xs[i] - xs[i-1] for each i on [1, 2, 3, 4]. What does print(xs) show?
0:57 · Python in 1 minute
x = [1, 2, 3], y = x, y[1] = 4. What does print(x) show?
0:58 · Python in 1 minute
x = 600, y = x, y = y + 2. Does x change? What does print(x, y) show?
0:57 · Python in 1 minute
x = [[1, 2], [3, 4]], y = x.copy(), y[0][0] = 99. What does print(x) show?
0:57 · Python in 1 minute
row = [0, 0, 0], matrix = [row, row, row], matrix[0][0] = 1. What prints?
0:57 · Python in 1 minute
a = [400, 500], b = [400, 500], c = a. What does print(a == b, a is b, a is c) show?
0:57 · Python in 1 minute
x = "hello", then x[0] = "a". What happens in Python?
0:57 · Python in 1 minute
Is "1234" greater than "23" in Python? True or False?
0:57 · Python in 1 minute
How do you print 5.136 as 5.14, or 1000 as 1,000, in Python?
0:57 · Python in 1 minute
x = "HELLo World", then x.lower(). Did x change?
0:56 · Python in 1 minute
How do you add up the numbers in the text "3,4,5,6,7,8,10" in Python?
0:57 · Python in 1 minute
Joining ["Hello", "World"] with a loop: where does the extra space come from?
0:57 · Python in 1 minute
What does "This is a good question.".replace("is", "IS") give in Python?
0:55 · Python in 1 minute
area_of_circle(10) + area_of_rect(5, 3): what does Python print?
0:57 · Python in 1 minute
A function prints "Imaginary." So what is stored in s1?
0:57 · Python in 1 minute
foo(0) matches neither the if nor the elif. What does it return?
0:56 · Python in 1 minute
Can a Python function return two values?
0:56 · Python in 1 minute
for i, c in enumerate("abc"): what does this loop print?
0:56 · Python in 1 minute
The same map printed twice. Why is the second list empty?
0:57 · Python in 1 minute
Remove every word from a list inside a for loop. What is left?
0:58 · Python in 1 minute
sum = 0 in a loop, then sum([1, 2, 3]). Why does Python raise an error?
0:58 · Python in 1 minute
What does list(map(int, ["1", "245", "129"])) print in Python?
0:57 · Python in 1 minute
derivative(f) returns a new function. What does derivative(square)(5) print?
0:57 · Python in 1 minute
f sets its own y = x, and y = 0 outside it. What do print(f(2)) and print(y) show?
0:55 · Python in 1 minute
y = 3 outside, and y = y + x inside f. What happens when you call f(2)?
0:57 · Python in 1 minute
x = 10 outside, and f(x) prints x, sets x = 20, prints x. What does f(5) print?
0:54 · Python in 1 minute
Two counters from one function, from 5 and 25. What does print(c1(), c2(), c1(), c2()) show?
0:57 · Python in 1 minute
h adds to a list it remembers. After h(1) to h(4), what does print(xs) show?
0:51 · Python in 1 minute
Ten functions made in a loop, each returning i + x. What do fs[0](1) and fs[5](1) give?
0:51 · Python in 1 minute
A for loop and a list comprehension build the squares of 1 to 10. Does xs == ys print True?
0:57 · Python in 1 minute
Does order matter? {2, 3, 5, 7, 11} == {3, 5, 7, 11, 2} and [1, 2, 3] == [2, 1, 3]
0:55 · Python in 1 minute
Three loops add 1, 3, 5, 7, 9. What does the for loop with range(1, 11, 2) print?
0:57 · Python in 1 minute
A dictionary of scores. What do scores["Beta"] and scores["beta"] print?
0:54 · Python in 1 minute
Count the words of "the quick brown fox jumps over the lazy dog". What is in freq?
0:56 · Python in 1 minute
Count the words of "The dog saw the dog." with a dictionary. Is "the" counted 2 times?
0:55 · Python in 1 minute
A dictionary code moves each letter 2 places. What does "map" become?
0:55 · Python in 1 minute
An API returns two errors: 404 and 500. Which one is your fault?
0:56 · Software tools for AI in 1 minute
A Pydantic model says year is an int. You pass the text "2010". What happens?
0:55 · Software tools for AI in 1 minute
Two annotators agree on 8 of 10 emails. Is 80% agreement impressive?
0:55 · Software tools for AI in 1 minute
Next word: great 60%, good 30%, fine 10%. At temperature 0, how often is it "great"?
0:56 · Software tools for AI in 1 minute
9 runs to tune 2 settings, and only one matters. Grid search or random search?
0:56 · Software tools for AI in 1 minute
A model has 7 billion weights, each a 32-bit float. How much memory do they need?
0:56 · Software tools for AI in 1 minute
An API sends {"year": "2010"}. What does data["year"] + 1 give?
0:56 · Software tools for AI in 1 minute
A request fails 4 times. You wait 1 s, then double the wait. Total wait before try 5?
0:57 · Software tools for AI in 1 minute
A page has 3 titles: 2 in movie cards, 1 in a sidebar. How many match ".movie .title"?
0:56 · Software tools for AI in 1 minute
A UTF-8 file says Amélie. You open it as Latin-1. What do you see?
0:57 · Software tools for AI in 1 minute
The header has 3 columns. How many pieces does split(",") cut this CSV row into?
0:56 · Software tools for AI in 1 minute
1,000 movies: 13 lack a year, 108 a rating, 366 box office. How many complete rows?
0:56 · Software tools for AI in 1 minute
Two annotators box one object, one box half a box right. Overlap ÷ area they cover?
0:55 · Software tools for AI in 1 minute
Model: 99% positive, 98% negative, 52% positive. Which review is worth a paid label?
0:57 · Software tools for AI in 1 minute
Rule A (right 80%) says positive; B (right 90%) says negative. Chance it's positive?
0:57 · Software tools for AI in 1 minute
10,000 labels: people at $0.30 each, an LLM at $0.002. How many times cheaper is it?
0:57 · Software tools for AI in 1 minute
Is it safe to turn a handwritten 6 upside down to get more training data?
0:55 · Software tools for AI in 1 minute
CutMix pastes a 16 × 16 dog patch into a 32 × 32 cat photo. What is the new label?
0:56 · Software tools for AI in 1 minute
Is flipping images left to right a safe way to get more training data?
0:56 · Software tools for AI in 1 minute
Free tier: 15 requests a minute. How long to label 900 reviews, one per request?
0:57 · Software tools for AI in 1 minute
A free tier gives 1 million tokens a day. About how many words is that?
0:56 · Software tools for AI in 1 minute
Predicting tomorrow's stock move: split 200 days at random into train and test?
0:56 · Software tools for AI in 1 minute
14 scans from 5 patients, split at random. Can one patient be in train and test?
0:55 · Software tools for AI in 1 minute
Grid search over 3 settings with 5-fold cross-validation. How many models to train?
0:57 · Software tools for AI in 1 minute
Same code, same data, and three runs give 92%, 85% and 88%. Why?
0:56 · Software tools for AI in 1 minute
Edit one line of app.py and rebuild the Docker image. Do all packages reinstall?
0:57 · Software tools for AI in 1 minute
Your project is 2 GB. You make a new Git branch. How big is the branch?
0:57 · Software tools for AI in 1 minute
Car-price model learned on 5,000 to 60,000 km. Now cars average 82,000 km. Its error?
0:56 · Software tools for AI in 1 minute
Shuffle the 10 values into two random groups of 5. How often is the gap 7.0 or more?
0:57 · Software tools for AI in 1 minute
Fraud rises from 1% to 5%. You still review only scores above 0.80. What happens?
0:56 · Software tools for AI in 1 minute
This code runs on every request and takes 2.4 s. Which step should you fix?
0:57 · Software tools for AI in 1 minute
In Python, is 0.1 + 0.2 == 0.3?
0:57 · Software tools for AI in 1 minute
A model file is 600 KB. You set its smallest 40% of weights to 0. Size now?
0:57 · Software tools for AI in 1 minute
An LLM "calls" get_weather("Gandhinagar"). Who actually runs that function?
0:57 · Software tools for AI in 1 minute
"Square root of 144, plus Mumbai's temperature in °F?" How many tool calls?
0:57 · Software tools for AI in 1 minute
A label file gives a car's box as [100, 200, 400, 300]. How wide is the car?
0:57 · Software tools for AI in 1 minute
"Not good at all" is labelled POSITIVE. A model that never saw it says 92% NEGATIVE. Wrong?
0:57 · Software tools for AI in 1 minute
A photo 400 px wide is flipped left to right. Its nose box ran 180 to 250. Where is it now?
0:57 · Software tools for AI in 1 minute
You try 100 tree depths and keep the best test score, 87%. Will new data give about 87%?
0:57 · Software tools for AI in 1 minute
You will try 100 learning rates from 0.001 to 1. Which way tries more values below 0.01?
0:57 · Software tools for AI in 1 minute
50 steps per trial. At step 2, trial 2 is at 46%, the others near 75%. Keep training it?
0:57 · Software tools for AI in 1 minute
You edit 3 files, run git add on one, then git commit. How many edits does the commit save?
0:57 · Software tools for AI in 1 minute
A Docker container saves model.pkl and exits. What is in outputs/ on your laptop?
0:58 · Software tools for AI in 1 minute
4 orders a week, Rs 500: premium in January, regular now. Will input charts show it?
0:57 · Software tools for AI in 1 minute
You open a Streamlit app and click Classify 10 times. How many times does the model load?
0:57 · Software tools for AI in 1 minute
A CI test needs accuracy above 0.85. Laptop: 0.86. CI server: 0.84. Is the code broken?
0:56 · Software tools for AI in 1 minute
A tiny 70 KB model is stored as 8-bit integers: now 22 KB. Does it run faster on a CPU?
0:57 · Software tools for AI in 1 minute
An LLM never saw your course notes, and they change weekly. How should it answer from them?
0:57 · Software tools for AI in 1 minute
An agent may call the model 3 times. The question needs 3 tool calls. Does it answer?
0:57 · Software tools for AI in 1 minute
Seven Probability Ideas That Power Machine Learning
8:00 · Seven Ideas in Machine Learning
Seven Linear Algebra Ideas That Power Machine Learning
8:20 · Seven Ideas in Machine Learning
Seven Numerical Computing Ideas Behind Modern Machine Learning
8:24 · Seven Ideas in Machine Learning
Seven Ways Machines Learn Representations
7:33 · Seven Ideas in Machine Learning
Seven Loss Functions That Shaped Machine Learning
8:21 · Seven Ideas in Machine Learning
Seven Ideas Behind Sequential Data
8:16 · Seven Ideas in Machine Learning
Learning Machines: 80 Years of Machine Learning in 2 Minutes
2:04 · Seven Ideas in Machine Learning
Seven Equations That Shaped Modern Machine Learning
2:08 · Seven Ideas in Machine Learning
Anna Mani: Measuring the Atmosphere | Indian Scientists, Episode 1
4:20 · Indian scientists
Multimodal AI Tutorial: Google Gemini API for Vision, Audio & Text | Python 2025
21:22 · Agentic AI
Agentic Coding: Jupyter notebooks and Quarto Documents
10:33 · Agentic AI
Agentic coding with Gemini (free tier!)
11:32 · Agentic AI
Running LLMs locally (Ollama + WebUI) Code Execution, HTML previews
20:29 · Agentic AI
Agentic AI: Creating a new branch and PR workflow
10:03 · Agentic AI
Agentic Coding: Roo Code, VS Code, Python, Architecture
24:01 · Agentic AI
Introduction to NumPy
1:16:32 · Python data science
Introduction to Pandas
1:08:30 · Python data science
Introduction to Matplotlib
54:27 · Python data science
Jupyter Widgets Tutorial: Transform Your Python Code from Static to Dynamic
7:22 · Python data science
PyTorch Distributions (Discrete)
1:12:55 · Python data science
Introduction to Streamlit: Local execution, Deploy to Hugging Face, PyTorch distributions
37:43 · Python data science
2d integration, Geogebra, Joint distributions
11:25 · Python data science
Deploying a simple HTML website on GitHub Pages
7:32 · GitHub Pages & websites
Forking an existing website repository to deploy your website to GitHub Pages
4:43 · GitHub Pages & websites
Setting up a static HTML website with Github Actions
3:03 · GitHub Pages & websites
Quarto: Template, Github Actions, Blog
9:46 · GitHub Pages & websites
CS 203: 1 - Data Collection for Machine Learning
1:22:15 · Software tools for AI · 2026
CS 203: 2 - Data Collection (Continued) -- HTML parsing
44:03 · Software tools for AI · 2026
CS 203: 3 - Data Validation
55:05 · Software tools for AI · 2026
Data labelling
1:13:08 · Software tools for AI · 2026
Optimizing Data Labeling: Active Learning, Weak Supervision & LLMs | CS 203 Week 4
1:16:31 · Software tools for AI · 2026
Data Augmentation for Deep Learning | CS 203
1:04:41 · Software tools for AI · 2026
Foundation Models in Practice | Gemini API Multimodal Tutorial | CS 203 Week 6
59:11 · Software tools for AI · 2026
Lecture 1: Introduction
1:17:10 · Machine learning · 2024
Lecture 2: Convention, Metrics, Classification, Regression
1:01:58 · Machine learning · 2024
Lecture 3: Decision Trees 1
1:15:04 · Machine learning · 2024
Lecture 4: Decision Trees 2, Bias and Variance
1:18:17 · Machine learning · 2024
Lecture 5: Cross validation
33:38 · Machine learning · 2024
Tutorial 1: Numpy, Pandas, Classes, Graphviz
46:26 · Machine learning · 2024
Lecture 6: Cross Validation, Ensemble Methods: Bagging, Boosting
1:17:29 · Machine learning · 2024
Lecture 7 + Tutorial 2: Random Forests, Meshgrid and Contour Plots
1:19:06 · Machine learning · 2024
Lecture 8: Linear Regression I
1:16:45 · Machine learning · 2024
Lecture 9 + Tutorial 3: Linear Regression II Basis Functions
1:15:16 · Machine learning · 2024
Lecture 10: Linear Regression III Geometric Interpretation
26:48 · Machine learning · 2024
Lecture 10: Linear Regression III Dummy Variables and Multi-colinearity
28:31 · Machine learning · 2024
Lecture 11 + Tutorial 4: Gradient Descent I
1:10:57 · Machine learning · 2024
Lecture 12: Stochastic Gradient Descent
1:18:05 · Machine learning · 2024
Lecture 13, Tutorial 5: Matrix Factorisation, Movie Recommendation
1:13:24 · Machine learning · 2024
Lecture 14: Regularized linear regression
1:14:46 · Machine learning · 2024
Lecture 15: Logistic Regression I
1:08:21 · Machine learning · 2024
Lecture 16: Logistic Regression II
39:18 · Machine learning · 2024
Lecture 17: Neural Networks I
40:38 · Machine learning · 2024
Lecture 18: Neural Networks II (Guest lecture Zeel B Patel)
1:12:57 · Machine learning · 2024
Lecture 19: Next token prediction using MLPs
44:55 · Machine learning · 2024
Lecture 20: Autograd
1:16:26 · Machine learning · 2024
Lecture 21: Convolutional Neural Network I
1:10:20 · Machine learning · 2024
Lecture 22: Convolutional Neural Network II
50:38 · Machine learning · 2024
Lecture 23: K Nearest Neighbors (and the Curse of Dimensionality)
1:06:53 · Machine learning · 2024
Lecture 24: Reinforcement Learning
1:08:35 · Machine learning · 2024
Lecture 25: Unsupervised Learning
57:49 · Machine learning · 2024
Lecture 26 +. Tutorial: Approximate KNN
1:13:41 · Machine learning · 2024
Lecture 27: Support Vector Machines I
1:11:25 · Machine learning · 2024
Lecture 28 + Tutorial: SVM II (Kernel trick)
1:05:56 · Machine learning · 2024
Lecture 29 and tutorial: SVM solution via QP; Kernels as measure of similarity
57:45 · Machine learning · 2024
Lecture 30: Soft margin SVM
1:11:17 · Machine learning · 2024
Lecture 31: Overall revision (quick walk through of previous 30 lectures)
1:15:54 · Machine learning · 2024
Lecture 1: Logistics and Introduction
1:21:18 · Probabilistic ML · 2023
Lecture 2: Bayes Rule in the context of ML
1:16:30 · Probabilistic ML · 2023
Lecture 3: Distributions, Sampling, Likelihood
1:11:20 · Probabilistic ML · 2023
Lecture 4: Log Likelihood of Univariate Distributions, Biased Estimators
1:16:01 · Probabilistic ML · 2023
Lecture 5: MLE for Linear And Logistic Regression
1:20:05 · Probabilistic ML · 2023
Lecture 6: MAP (coin toss), Multivariate Normal Distribution
1:17:55 · Probabilistic ML · 2023
Lecture 7: Bayesian Linear Regression
1:14:33 · Probabilistic ML · 2023
Lecture 8: Multivariate Calculus, Taylor's Series, Laplace Approximation
1:10:11 · Probabilistic ML · 2023
Lecture 9: Monte Carlo Sampling
58:13 · Probabilistic ML · 2023
Lecture 10: Monte Carlo Sampling, Sampling from common distributions
1:16:45 · Probabilistic ML · 2023
Lecture 11: Neural Networks Uncertainty - Aleatoric and Epistemic (Laplace Approximation)
1:11:58 · Probabilistic ML · 2023
Lecture 12: Rejection and Importance Sampling
1:17:10 · Probabilistic ML · 2023
Lecture 13: Practical MCMC (in Hamiltorch)
1:14:07 · Probabilistic ML · 2023
Lecture 14: MCMC theory - Markov chain, Properties, Metropolis Hastings
1:03:23 · Probabilistic ML · 2023
Lecture 15 : Model Calibration
1:15:23 · Probabilistic ML · 2023
Lecture 16: Deep Ensemble and Monte Carlo Dropout
1:05:46 · Probabilistic ML · 2023
Lecture 17: Bayesian Hierarchical Modelling
1:13:37 · Probabilistic ML · 2023
Lecture 18: Meta learning - Hypernetworks and Neural Processes
1:12:06 · Probabilistic ML · 2023
Lecture 20: Self-Supervised Learning
38:47 · Probabilistic ML · 2023
Lecture 21: Active Learning
1:09:54 · Probabilistic ML · 2023
Lecture 22: Bayesian Optimisation
1:14:41 · Probabilistic ML · 2023
Lecture 23: Variational Inference: Part 1
1:00:54 · Probabilistic ML · 2023
Lecture 24: Variational Inference: Part 2
1:17:20 · Probabilistic ML · 2023
Lecture 25: Variational Autoencoders
1:15:41 · Probabilistic ML · 2023
Introduction
1:17:19 · Machine learning · 2020
Convention, Metric, Classification, Regression, Decision Trees - 1
1:18:05 · Machine learning · 2020
Decision Trees 1
49:06 · Machine learning · 2020
Decision Trees II and Bias Variance I
45:46 · Machine learning · 2020
Ensemble Learning
1:16:12 · Machine learning · 2020
Linear Regression 1
1:18:31 · Machine learning · 2020
Geometric Interpretation of Least Squares
16:29 · Machine learning · 2020
Linear Regression II
37:50 · Machine learning · 2020
Maths for ML II: Contour Plot, Surface Plot, Gradient
11:48 · Machine learning · 2020
Gradient Descent
54:06 · Machine learning · 2020
Convexity
25:56 · Machine learning · 2020
Gradient Descent/Normal Equations Time Complexity
27:48 · Machine learning · 2020
Decision Tree (weighted samples)
12:39 · Machine learning · 2020
Constrained Optimisation and KKT conditions
56:38 · Machine learning · 2020
Ridge Regression I
46:32 · Machine learning · 2020
Ridge Regression II
26:05 · Machine learning · 2020
Lasso Regression 1
38:45 · Machine learning · 2020
Lasso Regression II
42:07 · Machine learning · 2020
Multivariate Normal Distribution 1
35:47 · Machine learning · 2020
Bayesian Learning
42:29 · Machine learning · 2020
Bayesian Linear Regression 1
1:17:56 · Machine learning · 2020
Bayesian Linear Regression 2
32:41 · Machine learning · 2020
Causality (Guest Lecture by Ritik Dutta)
47:52 · Machine learning · 2020
Bayesian Linear Regression 3
45:20 · Machine learning · 2020
Logistic Regression 1
31:25 · Machine learning · 2020
Deep Reinforcement Learning | Prof Shivaram Kalyanakrishnan | 2020
1:15:18 · Machine learning · 2020
Logistic Regression II
1:10:04 · Machine learning · 2020
Hidden Markov Model I
43:45 · Machine learning · 2020
Hidden Markov Model II
42:17 · Machine learning · 2020
Hidden Markov Model III
53:00 · Machine learning · 2020
Hidden Markov Model IV
36:59 · Machine learning · 2020
Hidden Markov Model V
37:16 · Machine learning · 2020
Entropy, Information Content, Conditional Entropy
27:52 · Machine learning · 2020
Differential Entropy, Mutual Information
16:49 · Machine learning · 2020
Gaussian Processes I
1:19:28 · Machine learning · 2020
Gaussian Processes Practical Demonstration
41:31 · Machine learning · 2020
Gaussian Processes Learning Parameters
21:16 · Machine learning · 2020
Neural Networks 1 (MLP, Perceptron)
58:11 · Machine learning · 2020
Neural Networks 2 (MLP convention)
28:29 · Machine learning · 2020
Neural Networks 3 (MLP Vectorisation, Forward Pass)
25:30 · Machine learning · 2020
Neural Networks 4 Back Propagation
1:22:32 · Machine learning · 2020
Neural Networks 5: CNNs
1:01:50 · Machine learning · 2020
Support Vector Machines 1
1:05:35 · Machine learning · 2020
Support Vector Machines 2: Kernel Functions
45:32 · Machine learning · 2020
Support Vector Machines 3: Soft Margin SVM
28:38 · Machine learning · 2020
Lagrangian and Duality (Minimax Inequality)
22:16 · Machine learning · 2020
Automatic Differentiation (Guest lecture by Varun Gohil)
22:06 · Machine learning · 2020
Naive Bayes
38:56 · Machine learning · 2020
KNN
36:14 · Machine learning · 2020
Unsupervised Learning
51:12 · Machine learning · 2020
Lecture 1: Introduction and Logistics
53:15 · Operating systems · 2018
Lecture 1: Introduction and Logistics (Audio missing)
41:23 · Operating systems · 2018
Lecture 2: Processes
42:07 · Operating systems · 2018
Lecture 3: The Process API (fork, wait, exec)
48:28 · Operating systems · 2018
Lecture 4: Revisiting the Linux Shell
37:36 · Operating systems · 2018
Lecture 5: CPU Scheduling 1
30:40 · Operating systems · 2018
Lecture 6: CPU Scheduling 2 (MLFQ)
55:33 · Operating systems · 2018
Lecture 6 (Addendum): Practice for scheduling policies (iPad recording)
14:48 · Operating systems · 2018
Lecture 7: CPU Scheduling 3 (MLFQ) + Limited Direct Execution
54:46 · Operating systems · 2018
Lecture 8: Limited Direct Execution + Memory Virtualisation Introduction
49:13 · Operating systems · 2018
Lecture 9: Memory Management Introduction
37:42 · Operating systems · 2018
Lecture 10: Memory Management (Base & Bounds) + Segmentation-1
55:50 · Operating systems · 2018
Lecture 11: Memory Management: Segmentation + Paging
49:07 · Operating systems · 2018
Lecture 12: Memory Management: Paging + TLB
52:06 · Operating systems · 2018
Lecture 13: Memory Management: TLB + Advanced Paging
53:36 · Operating systems · 2018
Lecture 14: Memory Management: Advanced Paging + Swapping
54:27 · Operating systems · 2018
Lecture 18: Quiz Discussion + Threads
48:37 · Operating systems · 2018
Lecture 19: Threads Locking #1
40:29 · Operating systems · 2018
Lecture 20: Advanced Locking
43:26 · Operating systems · 2018
Lecture 21: Condition Variables
41:33 · Operating systems · 2018
Lecture 22: Condition Variables (advanced)
47:14 · Operating systems · 2018
Lecture 23: Condition Variables + Semaphores
52:04 · Operating systems · 2018
Lecture 24: Semaphores + Deadlock
52:31 · Operating systems · 2018
Lecture 25: Concurrency Bugs
37:51 · Operating systems · 2018
Lecture 26: Semaphores + Concurrency Bugs
43:05 · Operating systems · 2018
Lecture 27: I/O devices + HDD
58:24 · Operating systems · 2018
Lecture 28: HDD + RAID
57:25 · Operating systems · 2018
Lecture 29: Raid Continued
27:28 · Operating systems · 2018
Lecture 30: Files and Directories - 1
49:33 · Operating systems · 2018
Lecture 31: Filesystems 2
26:01 · Operating systems · 2018
Lecture 32: Filesystems Implementation
41:46 · Operating systems · 2018
AISG Talk 4: Alok Talekar
1:26:57 · AI for social good · 2025
AISG Talk 3: Zeel B Patel on Object Detection
1:02:21 · AI for social good · 2025
AISG Talk 2: Anupam Sobti
1:12:52 · AI for social good · 2025
AISG Talk 1 Tavpritesh Sethi
1:15:44 · AI for social good · 2025
What happens when you enter a web address? From URL to pixels | How Things Work, Ep. 1
4:15 · How things work
From Attention to Applications: Encoder, Decoder and Cross-Attention | Deep Learning
51:43 · Deep learning · 2026
Self-Attention from First Principles | ES 667: Deep Learning
1:11:18 · Deep learning · 2026
Language Modeling: From Characters to Next-Token Prediction | ES 667: Deep Learning
1:06:54 · Deep learning · 2026
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Momentum in Deep Learning | ES 667: Deep Learning
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Optimization for Deep Learning | ES 667: Deep Learning
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