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Charotar University of Science and Technology
Faculty of Technology and Engineering
U & P U. Patel Department of Computer Engineering
Lesson Planning
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Academic Year :2026-27Semester: 5
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Subject Coordinator :Ronak PatelLec Hours/week:3
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 Subject Teacher Name: Ronak Patel, Mrugendra RahevarLab Hours/week:2
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Subject Code: CSUC301Subject Name:Machine Learning
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WeekUnit numberPlanned Faculty
Name		TopicsPlanned DatePlanned Hours
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12RNPIntroduction to ML, Linear Regression07/07/20261
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1MLRSystem of Linear Equations, Convex and Non-convex functions, Loss functions and its minimization
09/07/20261
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2RNPLinear regression cost function, gradient descent algorithm10/07/20261
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22RNPDerivative of Gradient descent and learning rate impact.14/07/20261
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1MLRGradient and Gradient Descent (Batch and SGD), Entropy and Information Gain, Bias and Variance Trade-offs
16/07/20261
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2RNPLinear regression evaluation metrics MAE, MSE, RMSE, R-square, with examples.17/07/20261
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32RNPLogistic regression, Linear regression vs Logistic regression21/07/20261
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1MLRCost function tread-offs and Regularization, Activation Functions: Which, When and Why
23/07/20261
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2RNPCost function, gradient descent24/07/20261
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42RNPEvaluation of Logistic regression accuracy, precision, recall, and F1 score with example.28/07/20261
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1MLRClustering: Overview of K-Means30/07/20261
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2RNPDecision Tree Entropy, Gini index31/07/20261
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52RNPExample of a Decision Tree04/08/20261
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1MLRHierarchical, DBSCAN06/08/20261
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2RNPEnsemble methods overview, Random Forest07/08/20261
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62RNPKNN11/08/20261
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3MLRGaussian Mixture Models, Hierarchical clustering13/08/20261
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2RNPSVM14/08/20261
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72RNPBayesian Method, Semi-supervised and Self-Supervised and their importance – Pseudo-labeling, Contrastive learning, overview of autoencoders18/08/20261
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3MLRClustering Evaluation / Validation: Silhouette Score, DBI, Calinski–Harabasz Index, ARI, Elbow20/08/20261
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4RNPIntroduction of NN, Perceptron, Forward pass21/08/20261
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84RNPBackpropogation algorithm, and derivative of it25/08/20261
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3MLRDimensionality Reduction: PCA including derivation, t-SNE, Reconstruction Error27/08/20261
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Declared Holiday — No Lecture28/08/2026
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94RNPTraining Optimization: SGD, Momentum, Adam01/09/20261
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3MLREvaluation Metrics and their interpretation/inference: Silhouette Score, Davies–Bouldin Index03/09/20261
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Declared Holiday — No Lecture04/09/2026
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10Sessional / Internal Examination Week (08/09/2026 to 12/09/2026): No Teaching
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114RNPRegularization: Dropout, Batch Normalization, Early Stopping15/09/20261
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3MLRCalinski–Harabasz Index, ARI, NMI, Homogeneity Score, Completeness Score, V-measure17/09/20261
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4RNPHyperparameter tuning: Optimization, Initialization, Architecture complexity, Evaluation Metrics and their interpretation/inference: Apart from common others are Learning curves, Convergence rate, Calibration error, Fairness metrics, and application domain specific18/09/20261
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125RNPIntroduction: Deep Learning Vs Machine Learning22/09/20261
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3MLRIntroduction: Agent, Environment, Reward24/09/20261
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5RNPConvolution NN: Operations, Pooling25/09/20261
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135RNP
1.1.   Transfer Learning: Pretrained models (TensorFlow, Keras, VGG, etc.), Fine tuning strategy
29/09/20261
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6MLRMarkov Decision Process01/10/20261
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Declared Holiday — No Lecture02/10/2026
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145RNP
Sequence Models: RNN intuition, Vanishing gradient problem
06/10/20261
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6MLRQ-Learning - Bellman equation intuition, Q-update rule derivation, Exploration vs Exploitation08/10/20261
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5RNPOverview of LSTM/GRU09/10/20261
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155RNPEvaluation Metrics and their interpretation/inference: Apart from common others are AUC-ROC13/10/20261
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6MLREvaluation Metrics and their interpretation/inference: Cumulative Reward, Average Episode Reward, Policy and Value Loss, Episode Length, Regret, Success Rate15/10/20261
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5RNP
Top-K Accuracy, Training Loss, Validation Loss, Convergence Rate, Learning Curves, and domain specific
16/10/20261
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16Declared Holiday — No Lecture20/10/2026
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6MLRRevision22/10/20261
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5RNPCNN Trainable parameter calculation23/10/20261
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